Abstract—This paper describes a new model-based speaker adaptation algorithm called the eigenvoice approach. The approach constrains the adapted model to be a linear combination of a small number of basis vectors obtained offline from a set of reference speakers, and thus greatly reduces the number of free parameters to be estimated from adaptation data. These “eigenvoice ” basis vectors are orthogonal to each other and guaranteed to represent the most important components of variation between the reference speakers. Experimental results for a small-vocabulary task (letter recognition) given in the paper show that the approach yields major improvements in performance for tiny amounts of adaptation data. For instance, we obtained 16% relative improvement in error rate with one letter of supervised adaptation data, and 26 % relative improvement with four letters of supervised adaptation data. After a comparison of the eigenvoice approach with other speaker adaptation algorithms, the paper concludes with a discussion of future work. Index Terms—Eigenvoice approach, principal component analysis, speaker adaptation, speaker clustering. I.

This paper describes our experiences with developing a realtime telephone-based speech recognizer as part of a conversational system in the weather information domain. This system has been used to collect spontaneous speech data which has proven to be extremely valuable for research in a number of different areas. After describing the corpus we have collected, we describe the development of the recognizer vocabulary, pronunciations, language and acoustic models for this system, the new weighted finite-state transducer–based lexical access component, and report on the current performance of the recognizer under several different conditions. We also analyze recognition latency to verify that the system performs in real time. 1.

This paper describes our experiences with developing a telephone-based speech recognizer as part of a conversational system in the weather information domain. This system has been used to collect spontaneous speech data which has proven to be extremely valuable for research in a number of different areas. After describing the corpus we have collected, we describe the development of the recognizer vocabulary, pronunciations, language and acoustic models for this system, and report on the current performance of the recognizer under several different conditions.

Summary In recent years, there has been a trend towards training large vocabulary continuous speech recognition (LVCSR) systems on a large amount of found data. Found data is recorded from spontaneous speech without careful control of the recording acoustic conditions, for example, conversational telephone speech. Hence, it typically has greater variability in terms of speaker and acoustic conditions than specially collected data. Thus, in addition to the desired speech variability required to discriminate between words, it also includes various non-speech variabil-ities, for example, the change of speakers or acoustic environments. The standard approach to handle this type of data is to train hidden Markov models (HMMs) on the whole data set as if all data comes from a single acoustic condition. This is referred to as multi-style training, for exam-ple speaker-independent training. Effectively, the non-speech variabilities are ignored. Though good performance has been obtained with multi-style systems, these systems account for all variabilities. Improvement may be obtained if the two types of variabilities in the found data are modelled separately. Adaptive training has been proposed for this purpose. In contrast to multi-style training, a set of transforms is used to represent the non-speech variabilities. A canonical

In this report, we present preliminary experiments towards automatic inference and evaluation of pronunciation models based on multiple utterances of each lexicon word and their given baseline pronunciation model (baseform phonetic transcription). In the present system, the pronunciation models are extracted by decoding each of the training utterances through a series of hidden Markov models (HMM), rst initialized to only allow the generation of the baseline transcription but iteratively relaxed to converge to a truly ergodic HMM. Each of the generated pronunciation models are then evaluated based on their con dence measure and their Levenshtein distance with the baseform model. The goal of this study is twofold. First, we show that this approach is appropriate to generate robust pronunciation variants. Second, we intend to use this approach to optimize these pronunciation models, by modifying/extending the acoustic features, to increase their con dence scores. In other words, while classical pronunciation modeling approaches usually attempt to make the models more and more complex to capture the pronunciation variability, we intend to x the pronunciation models and optimize the acoustic parameters to maximize their matching and discriminant properties.

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