Identifying non-linguistic speech features

Over the last decade technological advances have been made which enable us to envision real-world applications of speech technologies. It is possible to foresee applications, for example, information centers in public places such as train stations and airports, where the spoken query is to be recognized without even prior knowledge of the language being spoken. Other applications may require accurate identification of the speaker for security reasons, including control of access to confidential information or for telephone-based transactions. In this paper we present a unified approach to identifying nonlinguistic speech features from the recorded signal using phonebased acoustic likelihoods. The basic idea is to process the unknown speech signal by feature-specific phone model sets in parallel, and to hypothesize the feature value associated with the model set having the highest likelihood. This technique is shown to be effective for text-independent sex, speaker, and language identification and can enable better and more friendly humanmachine interaction. Text-independent speaker identification accuracies of 98.8% on TIMIT (168 speakers) and 99.2% on BREF (65 speakers), were obtained with one utterance per speaker, and 100% with 2 utterances for both corpora. Experiments estimating speaker-specific models without use of the phonetic transcription for the TIMIT speakers had the same identification accuracies obtained with the use of the transcriptions. French/English language identification is better than 99% with 2s of read, laboratory speech. On spontaneous telephone speech from the OGI corpus, the language can be identified as French or English with 82% accuracy with 10s of speech. 10 language identification using the OGI corpus is 59.7% with 10s of signal.