PLAN:

INTRODUCTION……………………………………..……………….………..…2

PRINCIPLES OF ASR TECHNOLOGY………………..……………….………..3
PERFORMANCE AND DESIGN ISSUES IN SPEECH APPLICATIONS……...7
CURRENT TRENDS IN VOICE-INTERACTIVE CALL………….…….………8
FUTURE TRENDS IN VOICE-INTERACTIVE CALL…….…..…………....…13
DEFINING AND ACQUIRING LITERACY IN THE AGE OF INFORMATION…………………………………….………………………..…..14
CONTENT-BASED INSTRUCTION AND LITERACY DEVELOPMENT…..15
THEORY INTO PRACTICE…………….……………………………………….17
CONCLUSION………………………………………………………...…………17
REFERENCES……………………………………………………………………18

INTRODUCTION

During the past two decades, the exercise of spoken language skills has received increasing attention among educators. Foreign language curricula focus on productive skills with special emphasis on communicative competence. Students' ability to engage in meaningful conversational interaction in the target language is considered an important, if not the most important, goal of second language education. This shift of emphasis has generated a growing need for instructional materials that provide an opportunity for controlled interactive speaking practice outside the classroom.
With recent advances in multimedia technology, computer-aided language learning (CALL) has emerged as a tempting alternative to traditional modes of supplementing or replacing direct student-teacher interaction, such as the language laboratory or audio-tape-based self-study. The integration of sound, voice interaction, text, video, and animation has made it possible to create self-paced interactive learning environments that promise to enhance the classroom model of language learning significantly. A growing number of textbook publishers now offer educational software of some sort, and educators can choose among a large variety of different products. Yet, the practical impact of CALL in the field of foreign language education has been rather modest. Many educators are reluctant to embrace a technology that still seeks acceptance by the language teaching community as a whole (Kenning & Kenning, 1990).
A number of reasons have been cited for the limited practical impact of computer-based language instruction. Among them are the lack of a unified theoretical framework for designing and evaluating CALL systems (Chapelle, 1997; Hubbard, 1988; Ng & Olivier, 1987); the absence of conclusive empirical evidence for the pedagogical benefits of computers in language learning (Chapelle, 1997; Dunkel, 1991; Salaberry, 1996); and finally, the current limitations of the technology itself (Holland, 1995; Warschauer, 1996). The rapid technological advances of the 1980s have raised both the expectations and the demands placed on the computer as a potential learning tool. Educators and second language acquisition (SLA) researchers alike are now demanding intelligent, user-adaptive CALL systems that offer not only sophisticated diagnostic tools, but also effective feedback mechanisms capable of focusing the learner on areas that need remedial practice. As Warschauer puts it, a computerized language teacher should be able to understand a user's spoken input and evaluate it not just for correctness but also for appropriateness. It should be able to diagnose a student's problems with pronunciation, syntax, or usage, and then intelligently decide among a range of options (e.g., repeating, paraphrasing, slowing down, correcting, or directing the student to background explanations). (Warschauer, 1996, p. 6)
Salaberry (1996) demands nothing short of a system capable of simulating the complex socio-communicative competence of a live tutor--in other words, the linguistic intelligence of a human--only to conclude that the attempt to create an "intelligent language tutoring system is a fallacy" (p. 11). Because speech technology isn't perfect, it is of no use at all. If it "cannot account for the full complexity of human language," why even bother modeling more constrained aspects of language use (Higgins, 1988, p. vii)? This sort of all-or-nothing reasoning seems symptomatic of much of the latest pedagogical literature on CALL. The quest for a theoretical grounding of CALL system design and evaluation (Chapelle, 1997) tends to lead to exaggerated expectations as to what the technology ought to accomplish. When combined with little or no knowledge of the underlying technology, the inevitable result is disappointment.

 

PRINCIPLES OF ASR TECHNOLOGY

Consider the following four scenarios:
1.                 A court reporter listens to the opening arguments of the defense and types the words into a steno-machine attached to a word-processor.
2.                 A medical doctor activates a dictation device and speaks his or her patient's name, date of birth, symptoms, and diagnosis into the computer. He or she then pushes "end input" and "print" to produce a written record of the patient's diagnosis.
3.                 A mother tells her three-year old, "Hey Jimmy, get me my slippers, will you?" The toddler smiles, goes to the bedroom, and returns with papa's hiking boots.
4.                 A first-grader reads aloud a sentence displayed by an automated Reading Tutor. When he or she stumbles over a difficult word, the system highlights the word, and a voice reads the word aloud. The student repeats the sentence--this time correctly--and the system responds by displaying the next sentence.
At some level, all four scenarios involve speech recognition. An incoming speech signal elicits a response from a "listener." In the first two instances, the response consists of a written transcript of the spoken input, whereas in the latter two cases, an action is performed in response to a spoken command. In all four cases, the "success" of the voice interaction is relative to a given task as embodied in a set of expectations that accompany the input. The interaction succeeds when the response--by a machine or human "listener"--matches these expectations.
Recognizing and understanding human speech requires a considerable amount of linguistic knowledge: a command of the phonological, lexical, semantic, grammatical, and pragmatic conventions that constitute a language. The listener's command of the language must be "up" to the recognition task or else the interaction fails. Jimmy returns with the wrong items, because he cannot yet verbally discriminate between different kinds of shoes. Likewise, the reading tutor would miserably fail in performing the court-reporter's job or transcribing medical patient information, just as the medical dictation device would be a poor choice for diagnosing a student's reading errors. On the other hand, the human court reporter--assuming he or she is an adult native speaker--would have no problem performing any of the tasks mentioned under (1) through (4). The linguistic competence of an adult native speaker covers a broad range of recognition tasks and communicative activities. Computers, on the other hand, perform best when designed to operate in clearly circumscribed linguistic sub-domains.
Humans and machines process speech in fundamentally different ways (Bernstein & Franco, 1996). Complex cognitive processes account for the human ability to associate acoustic signals with meanings and intentions. For a computer, on the other hand, speech is essentially a series of digital values. However, despite these differences, the core problem of speech recognition is the same for both humans and machines: namely, of finding the best match between a given speech sound and its corresponding word string. Automatic speech recognition technology attempts to simulate and optimize this process computationally.
Since the early 1970s, a number of different approaches to ASR have been proposed and implemented, including Dynamic Time Warping, template matching, knowledge-based expert systems, neural nets, and Hidden Markov Modeling (HMM) (Levinson & Liberman, 1981; Weinstein, McCandless, Mondshein, & Zue, 1975; for a review, see Bernstein & Franco, 1996). HMM-based modeling applies sophisticated statistical and probabilistic computations to the problem of pattern matching at the sub-word level. The generalized HMM-based approach to speech recognition has proven an effective, if not the most effective, method for creating high-performance speaker-independent recognition engines that can cope with large vocabularies; the vast majority of today's commercial systems deploy this technique. Therefore, we focus our technical discussion on an explanation of this technique.
An HMM-based speech recognizer consists of five basic components: (a) an acoustic signal analyzer which computes a spectral redivsentation of the incoming speech; (b) a set of phone models (HMMs) trained on large amounts of actual speech data; (c) a lexicon for converting sub-word phone sequences into words; (d) a statistical language model or grammar network that defines the recognition task in terms of legitimate word combinations at the sentence level; (e) a decoder, which is a search algorithm for computing the best match between a spoken utterance and its corresponding word string. Figure 1 shows a schematic redivsentation of the components of a speech recognizer and their functional interaction.

Figure 1. Components of a speech recognition device

A. Signal Analysis

The first step in automatic speech recognition consists of analyzing the incoming speech signal. When a person speaks into an ASR device--usually through a high quality noise-canceling microphone--the computer samples the analog input into a series of 16- or 8-bit values at a particular sampling frequency (ranging from 8 to 22KHz). These values are grouped together in divdetermined overlapping temporal intervals called "frames." These numbers provide a divcise description of the speech signal's amplitude. In a second step, a number of acoustically relevant parameters such as energy, spectral features, and pitch information, are extracted from the speech signal (for a visual redivsentation of some of these parameters, see Figure 2 on page 53). During training, this information is used to model that particular portion of the speech signal. During recognition, this information is matched against the div-existing model of the signal.

B. Phone Models

Training a machine to recognize spoken language amounts to modeling the basic sounds of speech (phones). Automatic speech recognition strings together these models to form words. Recognizing an incoming speech signal involves matching the observed acoustic sequence with a set of HMM models. An HMM can model either phones or other sub-word units or it can model words or even whole sentences. Phones are either modeled as individual sounds--so-called monophones--or as phone combinations that model several phones and the transitions between them (biphones or triphones). After comparing the incoming acoustic signal with the HMMs redivsenting the sounds of language, the system computes a hypothesis based on the sequence of models that most closely resembles the incoming signal. The HMM model for each linguistic unit (phone or word) contains a probabilistic redivsentation of all the possible pronunciations for that unit--just as the model of the handwritten cursive b would have many different redivsentations. Building HMMs--a process called training--requires a large amount of speech data of the type the system is expected to recognize. Large-vocabulary speaker-independent continuous dictation systems are typically trained on tens of thousands of read utterances by a cross-section of the population, including members of different dialect regions and age-groups. As a general rule, an automatic speech recognizer cannot correctly process speech that differs in kind from the speech it has been trained on. This is why most commercial dictation systems, when trained on standard American English, perform poorly when encountering accented speech, whether by non-native speakers or by speakers of different dialects. We will return to this point in our discussion of voice-interactive CALL applications.

C. Lexicon

The lexicon, or dictionary, contains the phonetic spelling for all the words that are expected to be observed by the recognizer. It serves as a reference for converting the phone sequence determined by the search algorithm into a word. It must be carefully designed to cover the entire lexical domain in which the system is expected to perform. If the recognizer encounters a word it does not "know" (i.e., a word not defined in the lexicon), it will either choose the closest match or return an out-of-vocabulary recognition error. Whether a recognition error is registered as a misrecognition or an out-of-vocabulary error depends in part on the vocabulary size. If, for example, the vocabulary is too small for an unrestricted dictation task--let's say less than 3K--the out-of-vocabulary errors are likely to be very high. If the vocabulary is too large, the chance of misrecognition errors increases because with more similar-sounding words, the confusability increases. The vocabulary size in most commercial dictation systems tends to vary between 5K and 60K.

D. The Language Model

The language model divdicts the most likely continuation of an utterance on the basis of statistical information about the frequency in which word sequences occur on average in the language to be recognized. For example, the word sequence A bare attacked him will have a very low probability in any language model based on standard English usage, whereas the sequence A bear attacked him will have a higher probability of occurring. Thus the language model helps constrain the recognition hypothesis produced on the basis of the acoustic decoding just as the context helps decipher an unintelligible word in a handwritten note. Like the HMMs, an efficient language model must be trained on large amounts of data, in this case texts collected from the target domain.
In ASR applications with constrained lexical domain and/or simple task definition, the language model consists of a grammatical network that defines the possible word sequences to be accepted by the system without providing any statistical information. This type of design is suitable for CALL applications in which the possible word combinations and phrases are known in advance and can be easily anticipated (e.g., based on user data collected with a system div-prototype). Because of the a priori constraining function of a grammar network, applications with clearly defined task grammars tend to perform at much higher accuracy rates than the quality of the acoustic recognition would suggest.

E. Decoder

Simply put, the decoder is an algorithm that tries to find the utterance that maximizes the probability that a given sequence of speech sounds corresponds to that utterance. This is a search problem, and especially in large vocabulary systems careful consideration must be given to questions of efficiency and optimization, for example to whether the decoder should pursue only the most likely hypothesis or a number of them in parallel (Young, 1996). An exhaustive search of all possible completions of an utterance might ultimately be more accurate but of questionable value if one has to wait two days to get a result. Trade-offs are therefore necessary to maximize the search results while at the same time minimizing the amount of CPU and recognition time.

 

PERFORMANCE AND DESIGN ISSUES IN SPEECH APPLICATIONS

For educators and developers interested in deploying ASR in CALL applications, perhaps the most important consideration is recognition performance: How good is the technology? Is it ready to be deployed in language learning? These questions cannot be answered except with reference to particular applications of the technology, and therefore touch on a key issue in ASR development: the issue of human-machine interface design.

While such systems are an important step in the right direction, other more complex and ambitious applications are conceivable and no doubt desirable. Imagine a student being able to access the Internet, find the language of his or her choice, and tap into a comdivhensive voice-interactive multimedia language program that would provide the equivalent of an entire first year of college instruction. The computer would evaluate the student's proficiency level and design a course of study tailored to his or her needs. Or think of using the same Internet resources and a set of high-level authoring tools to put together a series of virtual encounters surrounding the task of finding an apartment in Berlin. As a minimum, one would hope that natural speech input capacity becomes a routine feature of any CALL application.
To many educators, these may still seem like distant goals, and yet we believe that they are not beyond reach. In what follows, we identify four of the most persistent issues in building speech-enabled language learning applications and suggest how they might be resolved to enable a more widesdivad commercial implementation of speech technology in CALL.

1. More research is necessary on modeling and divdicting multi-turn dialogs.

An intelligent open response language tutor must not only correctly recognize a given speech input, but in addition understand what has been said and evaluate the meaning of the utterance for pragmatic appropriateness. Automatic speech understanding requires Natural Language Processing (NLP) capabilities, a technology for extracting grammatical, semantic, and pragmatic information from written or spoken discourse. NLP has been successfully deployed in expert systems and information retrieval. One of the first voice-interactive dialog systems using NLP was the DARPA-sponsored Air Travel Information System (Pallett, 1995), which enables the user to obtain flight information and make ticket reservations over the telephone. Similar commercial systems have been implemented for automatic retrieval of weather and restaurant information, virtual environments, and telephone auto-attendants. Many of the lessons learned in developing such systems can be valuable for designing CALL applications for practicing conversational skills.

2. More and better training data are needed to support basic research on modeling non-native conversational speech.

One of the most needed resources for developing open response conversational CALL applications is large corpora of non-native transcribed speech data, of both read and conversational speech. Since accents vary depending on the student's first language, separate databases must either be collected for each L1 subgroup, or a redivsentative sample of speakers of different languages must be included in the database. Creating such databases is extremely labor and cost intensive--a phone level transcription of spontaneous conversational data can cost up to one dollar per phone. A number of multilingual conversational databases of telephone speech are publicly available through the Linguistic Data Consortium (LDC), including Switchboard (US English) and CALLHOME (English, Japanese, Spanish, Chinese, Arabic, German). Our own effort in collaboration with John Hopkins University (Byrne, Knodt, Khudanpur, & Bernstein, 1998; Knodt, Bernstein, & Todic,1998) has been to collect and model spontaneous English conversations between Hispanic natives. All of these efforts will improve our understanding of the disfluent speech of language learners and help model this speech type for the purpose of human-machine communication.
DEFINING AND ACQUIRING LITERACY IN THE AGE OF INFORMATION
Moll defined literacy as "a particular way of using language for a variety of purposes, as a sociocultural practice with intellectual significance" (1994, p. 201). While traditional definitions of literacy have focused on reading and writing, the definition of literacy today is more complex. The process of becoming literate today involves more than learning how to use language effectively; rather, the process amplifies and changes both the cognitive and the linguistic functioning of the individual in society. One who is literate knows how to gather, analyze, and use information resources to solve problems and make decisions, as well as how to learn both independently and cooperatively. Ultimately literate individuals possess a range of skills that enable them to participate fully in all aspects of modern society, from the workforce to the family to the academic community. Indeed, the development of literacy is "a dynamic and ongoing process of perpetual transformation" (Neilsen, 1989, p. 5), whose evolution is influenced by a person's interests, cultures, and experiences. Researchers have viewed literacy as a multifaceted concept for a number of years (Johns, 1997). However, succeeding in a digital, information-oriented society demands multiliteracies, that is, competence in an even more diverse set of functional, academic, critical, and electronic skills.
To be considered multiliterate, students today must acquire a battery of skills that will enable them to take advantage of the diverse modes of communication made possible by new technologies and to participate in global learning communities. Although becoming multiliterate is not an easy task for any student, it is especially difficult for ESL students operating in a second language. In their attempts to become multiliterate, ESL students must acquire linguistic competence in a new language and at the same time develop the cognitive and sociocultural skills necessary to gain access into the social, academic, and workforce environments of the 21^st century. They must become functionally literate, able to speak, understand, read, and write English, as well as use English to acquire, articulate and expand their knowledge. They must also become academically literate, able to read and understand interdisciplinary texts, analyze and respond to those texts through various modes of written and oral discourse, and expand their knowledge through sustained and focused research. Further, they must become critically literate, defined here as the ability to evaluate the validity and reliability of informational sources so that they may draw appropriate conclusions from their research efforts. Finally, in our digital age of information, students must become electronically literate, able "to select and use electronic tools for communication, construction, research, and autonomous learning" (Shetzer, 1998).
Helping students develop the range of literacies they need to enter and succeed at various levels of the academic hierarchy and subsequently in the workforce requires a pedagogy that facilitates and hastens linguistic proficiency development, familiarizes students with the requirements and conventions of academic discourse, and supports the use of critical thinking and higher order cognitive processes. A large body of research conducted over the past decade (see, e.g., Benesch, 1988; Brinton, Snow, & Wesche, 1989; Crandall, 1993; Kasper, 1997a, 2000a; Pally, 2000; Snow & Brinton, 1997) has shown that content-based instruction (CBI) is highly effective in helping ESL students develop the literacies they need to be successful in academic and workforce environments.
CONTENT-BASED INSTRUCTION AND LITERACY DEVELOPMENT
CBI develops linguistic competence and functional literacy by exposing ESL learners to interdisciplinary input that consists of both "everyday" communicative and academic language (Cummins, 1981; Mohan, 1990; Spanos, 1989) and that contains a wide range of vocabulary, forms, registers, and pragmatic functions (Snow, Met, & Genesee, 1989; Zuengler & Brinton, 1997). Because content-based pedagogy encourages students to use English to gather, synthesize, evaluate, and articulate interdisciplinary information and knowledge (Pally, 1997), it also allows them to hone academic and critical literacy skills as they practice appropriate patterns of academic discourse (Kasper, 2000b) and become familiar with sociolinguistic conventions relating to audience and purpose (Soter, 1990).
The theoretical foundations supporting a content-based model of ESL instruction derive from cognitive learning theory and second language acquisition (SLA) research. Cognitive learning theory posits that in the process of acquiring literacy skills, students progress through a series of three stages, the cognitive, the associative, and the autonomous (Anderson, 1983a). Progression through these stages is facilitated by scaffolding, which involves providing extensive instructional support during the initial stages of learning and gradually removing this support as students become more proficient at the task (Chamot & O'Malley, 1994). Second language acquisition (SLA) research emphasizes that literacy development can be facilitated by providing multiple opportunities for learners to interact in communicative contexts with authentic, linguistically challenging materials that are relevant to their personal and educational goals (see, e.g., Brinton, et al., 1989; Kasper, 2000a; Krashen, 1982; Snow & Brinton, 1997; Snow, et al., 1989).
In a 1996 paper published in The Harvard Educational Review, The New London Group (NLG) advocated developing multiliteracies through a pedagogy that involves a complex interaction of four factors which they called Situated Practice, Overt Instruction, Critical Framing, and Transformed Practice. According to the NLG, becoming multiliterate requires critical engagement in relevant tasks, interaction with diverse forms of communication made possible by electronic technologies, and participation in collaborative learning contexts. Warschauer (1999) concurred and stated that a pedagogy of critical inquiry and problem solving that provides the context for "authentic and collaborative projects and analyses" (p. 16) that support and are supported by the use of electronic technologies is necessary for ESL students to acquire the linguistic, social, and technological competencies key to literacy in a digital world.
According to a 1995 report published by the United States Department of Education, "technology is an important enabler for classes organized around complex, authentic tasks" and when "used in support of challenging projects, [technology] can contribute to students' sense ... that they are using real tools for real purposes." Technology use increases students' motivation as it promotes their active engagement with language and content through authentic, challenging tasks that are interdisciplinary in nature (McGrath, 1998). Technology use also encourages students to spend more time on task. As they search for information in a hyperlinked environment, ESL students benefit from increased opportunities to process linguistic and content information. Used as a tool for learning, technology supports a level of task authenticity and complexity that fits well with the interdisciplinary work inherent in content-based instruction and that promotes the acquisition of multiliteracies.
THEORY INTO PRACTICE
These research findings suggest that in our efforts to divpare ESL students for the challenges of the academic and workforce environments of the 21^st century, we should adopt a pedagogical model that incorporates information technology as an integral component and that specifically targets the development of the range of literacies deemed necessary for success in a digital, information-oriented society. This paper describes a content-based pedagogy, which I call focus discipline research (Kasper, 1998a), and divsents the results of a classroom study conducted to measure the effects of focus discipline research on the development of ESL students' literacy skills.
As described here, focus discipline research puts theory into practice as it incorporates the principles of cognitive learning theory, SLA research, and the four components of the NLG's (1996) pedagogy of multiliteracies. Through pedagogical activities that provide the context for situated practice, overt instruction, critical framing, and transformed practice, focus discipline research promotes ESL students' choice of and responsibility for course content, engages them in extended practice with linguistic structures and interdisciplinary material, and encourages them to become "content experts" in a subject of their own choosing.

CONCLUSION
It can be seen that it is difficult and probably undesirable to attempt to determine the difficulty of a listening and viewing task in any absolute terms. By considering the three aspects that affect the level of difficulty, namely text, task, and context features, it is possible to identify those characteristics of tasks that can be manipulated. Having identified the variable characteristics of tasks in developing the model, it is necessary to look to the dynamic interaction among, tasks, texts, and the computer-based environment.
Task design and text selection in this model also incorporate the identification and consideration of context. Teachers can make provision for their influence on learner perception of difficulty by providing texts and tasks that range across these levels, and by ensuring that learners with lower language proficiency can ease themselves gradually into the more contextually difficult tasks. This can be achieved by reducing the level of difficulty of other parameters such as text or task difficulty, or by minimizing other aspects of contextual difficulty. Thus, for example, learners of lower proficiency who are exposed for the first time to a task based on a broadcast announcement would be provided with appropriate visual support in the form of graphics or video to reduce textual difficulty. The task type would also be kept to a low level of cognitive demand (Hoven, 1991, 1997a, 1997b).
In a CELL environment, this identification of parameters of difficulty enables task designers to develop and modify tasks on the basis of clear language pedagogy that is both learner-centred and cognitively sound. Learners are provided with the necessary information on text, task, and context to make informed choices, and are given opportunities to implement their decisions. Teachers are therefore creating a CELL environment that facilitates and encourages exploration of, and experimentation with, the choices available. Within this model, learners are then able to adjust their own learning paths through the texts and tasks, and can do this at their own pace and at their individual points of readiness. In sociocultural terms, the model provides learners with a guiding framework or community of practice within which to develop through their individual Zones of Proximal Development. The model provides them with the tools to mediate meaning in the form of software incorporating information, feedback, and appropriate help systems.
By taking account of learners' needs and making provision for learner choice in this way, one of the major advantages of using computers in language learning--their capacity to allow learners to work at their own pace and in their own time--can be more fully exploited. It then becomes our task as researchers to evaluate, with learners' assistance, the effectiveness of environments such as these in improving the their listening and viewing comdivhension as well as their approaches to learning in these environments.

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