Vocabulary and Environment Adaptation in Vocabulary-Independent Speech Recognition

Vocabulary and Environment Adaptation in Vocabulary-Independent Speech Recognition Hsiao-Wuen Hon Kai-Fu Lee School of Computer Science Carnegie Mellon University Pittsburgh, Pennsylvania 15213 Speech & Language Group Apple Computer, Inc. Cupertino, CA 95014 1 Abstract In this paper, we are looking into the adaptation issues of vocabulary-independent (VI) systems. Just as with speaker- adaptation in speaker-independent system, two vocabulary adaptation algorithms [5] are implemented in order to tailor the VI subword models to the target vocabulary. The first algorithm is to generate vocabulary-adapted clustering de- cision trees by focusing on relevant allophones during tree generation and reduces the VI error rate by 9%. The second algorithm, vocabulary-bias training, is to give the relevant allophones more prominence by assign more weight to them during Baum-Welch training of the generalized allophonic models and reduces the VI error rate by 15%. Finally, in order to overcome the degradation caused by the different acoustic environments used for VI training and testing, CDCN and ISDCN originally designed for microphone adaptation are in- corporated into our VI system and both reduce the degradation of VI cross-environment recognition by 50%. 2 Introduction In 89' and 91' DARPA Speech and Natural Language Work- shops [8, 7], we have shown that accurate vocabulary- independent (VI) speech recognition is possible. However, there are many anatomical differences between tasks (vocab- ularies), such as the size of the vocabulary and the frequency of confusable words., which might affect the acoustic model- ing techniques to achieve optimal performance in vocabulary- dependent (VD) systems. For example, whole-word models are often used in small-vocabulary tasks, while subword mod- els must be used in large-vocabulary tasks. Moreover, within a limited vocabulary, it is possible to design some special fea- tures to separate the confusable models. Therefore, discrimi- native training techniques, such as neural networks [10], and maximum mutual information estimator (MMIE) [4], have so much success in small-vocabulary tasks. Just as with speaker adaptation in speaker-independent systems, it is desirable to implement vocabulary adapta- tion to make the VI system tailored to the target vocabulary (task). Our first vocabulary adaptation algorithm is to build vocabulary-adapted allophonic clustering decision trees for 168 the target vocabulary based on only the relevant allophones. The adapted trees would only focus on the relevant contexts to separate the relevant allophones, thus give the resulting allophonic clusters more discriminative power for the target vocabulary. In an experiment of adapting allophone cluster- ing tree for the Resource Management task, this algorithm achieved an 9% error reduction. Our second vocabulary adaptation algorithm is to focus on the relevant allophones during training of generalized allo- phonic models, instead of focusing on them during generation of allophonic clustering decision trees. To achieve that, we give the relevant allophones more prominence by assigning more weight to the relevant allophones during Baum-Welch training of generalized allophonic models. With vocabulary- bias training we are able to reduce the VI error rate by 15% for the Resource Management task. We have found that different recording environments be- tween training and testing …

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