Identification of Non-Linguistic Speech Features

Identification of Non-Linguistic Speech Features Jean-Luc Gauvain and Lori F. Lamel LIMSI-CNRS, BP 133 91403 Orsay cedex, FRANCE {lamel,gauvain}@limsi.fr ABSTRACT Over the last decade technological advances have been made which en- able us to envision real-world applications of speech technologies. It is possible to foresee applications where the spoken query is to be recognized without even prior knowledge of the language being spoken, for exana- pie, information centers in public places such as train stations and airports. Other applications may require accurate identification of the speaker for se- curity reasons, including control of access to confidential information or for telephone-based transactions. Ideally, the speaker's identity can be verified continually during the transaction, in a manner completely transparent to the user. With these views in mind, this paper presents a unified approach to identifying non-linguistic speech features from the recorded signal using phone-based acoustic likelihoods. This technique is shown to be effective for text-independent language, sex, and speaker identification and can enable better and more friendly human-machine interaction. With 2s of speech, the language can be identi- fied with better than 99% accuracy. Error in sexddentification is about 1% on a per-sentence basis, and speaker identification accuracies of 98.5% on 'lIMIT (168 speakers) and 99.2% on BREF (65 speakers), were obtained with one utterance per speaker, and 100% with 2 utterances for both corpora. An experiment using unsupervised adaptation for speaker identification on the 168 TIMIT speakers had the same identification accuracies obtained with supervised adaptation. INTRODUCTION As speech recognition technology advances, so do the aims of system designers, and the prospects of potential ap- plications. One of the main efforts underway in the com- munity is the development of speaker-independent, task- independent large vocabulary speech recognizers that can easily be adapted to new tasks. It is becoming apparent that many of the portability issues may depend more on the specification of the task, and the ergonomy, than on the per- formance of the speech recognition component itself. The acceptance of speech technology in the world at large will depend on how well the technology can be integrated in sys- tems which simplify the life of the users. This in turns means that the service provided by such a system must be easy to use, and as fast as other providers of the service (i.e., such as using a human operator). While the focus has been on improving the performance of the speech recognizers, it is also of interest to be able to identify what we refer to as some of the "non-linguistic" speech features present in the acoustic signal. For example, it is possible to envision applications where the spoken query is to be recognized without prior knowledge of the language being spoken. This is the case for information centers in public places, such as train stations and airports, where the language may change from one user to the next. The ability to automatically identify the language being spoken, and to respond appropriately, is possible. Other applications, such as for …

Content truncated to 500 words