In this paper we report on progress made at LIMSI in speaker-independent large vocabulary speech dictation using newspaper-based speech corpora in English and French. The recognizer makes use of continuous density HMMs with Gaussian mixtures for acoustic modeling and n-gram statistics estimated on newspaper texts for language modeling. Acoustic modeling uses cepstrum-based features, context-dependent phone models (intra and interword), phone duration models, and sex-dependent models. For English the ARPA Wall Street Journal-based CSR corpus is used and for French the BREF corpus containing recordings of texts from the French newspaper Le Monde is used. Experiments were carried out with both these corpora at the phone level and at the word level with vocabularies containing up to 20,000 words. Word recognition experiments are also described for the ARPA RM task which has been widely used to evaluate and compare systems.

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 languagebeing 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.

This paper presents some of the recent research on speaker-independent continuous phone recognition for both French and English. The phone accuracy is assessed on the BREF corpus for French, and on the Wall Street Journal and TIMIT corpora for English. Cross-language differences concerning language properties are presented. It was found that French is easier to recognize at the phone level (the phone error for BREF is 23.6% vs. 30.1% for WSJ), but harder to recognize at the lexical level due to the larger number of homophones. Experiments with signal analysis indicate that a 4kHz signal bandwidth is sufficient for French, whereas 8kHz is needed for English. Phone recognition is a powerful technique for language, sex, and speaker identification. With 2s of speech, the languagecan be identified with better than 99% accuracy. Sex-identification for BREF and WSJ is errorfree. Speaker identification accuracies of 98.2% on TIMIT (462 speakers) and 99.1% on BREF (57 speakers), were obtained w...

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 where the spoken query is to be recognized without even prior knowledge of the language being spoken, for example, information centers in public places such as train stations and airports. Other applications may require accurate identification of the speaker for security 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 h...

A major axis of research at LIMSI is directed at multilingual, speaker-independent, large vocabulary speech dictation. In this pa-per the LIMSI recognizer which was evaluated in the ARPA NOV93 CSR test is described, and experimental results on the WSJ and BREF corpora under closely matched conditions are reported. For both corpora word recognition expenrnents were carried out with vocabularies containing up to 20k words. The recognizer makes use of continuous density HMM with Gaussian mixture for acous-tic modeling and n-gram statistics estimated on the newspaper texts for language modeling. The recognizer uses a time-synchronous graph-search strategy which is shown to still be viable with a 20k-word vocabulary when used with bigram back-off language models. A second forward pass, which makes use of a word graph generated with the bigram, incorporates a trigram language model. Acoustic modeling uses cepstrum-based features, context-dependent phone models (intra and interword), phone duration models, and sex-dependent models.

A basic overview is presented of the main ongoing efforts in large vocabulary, continuous speech recognition (LVCSR) for European languages. We address issues in acoustic modeling, lexical representation, and language modeling for several European languages, as well as issues in comparative evaluation.