Multilingual Perception and Recognition 1
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1: ICSLP'98 Proceedings
Keynote SpeechesText-To-Speech Synthesis 1Spoken Language Models and Dialog 1Prosody and Emotion 1Hidden Markov Model Techniques 1Speaker and Language Recognition 1Multimodal Spoken Language Processing 1Isolated Word RecognitionRobust Speech Processing in Adverse Environments 1Spoken Language Models and Dialog 2Articulatory Modelling 1Talking to Infants, Pets and LoversRobust Speech Processing in Adverse Environments 2Spoken Language Models and Dialog 3Speech Coding 1Articulatory Modelling 2Prosody and Emotion 2Neural Networks, Fuzzy and Evolutionary Methods 1Utterance Verification and Word Spotting 1 / Speaker Adaptation 1Text-To-Speech Synthesis 2Spoken Language Models and Dialog 4Human Speech Perception 1Robust Speech Processing in Adverse Environments 3Speech and Hearing Disorders 1Prosody and Emotion 3Spoken Language Understanding Systems 1Signal Processing and Speech Analysis 1Spoken Language Generation and Translation 1Spoken Language Models and Dialog 5Segmentation, Labelling and Speech Corpora 1Multimodal Spoken Language Processing 2Prosody and Emotion 4Neural Networks, Fuzzy and Evolutionary Methods 2Large Vocabulary Continuous Speech Recognition 1Speaker and Language Recognition 2Signal Processing and Speech Analysis 2Prosody and Emotion 5Robust Speech Processing in Adverse Environments 4Segmentation, Labelling and Speech Corpora 2Speech Technology Applications and Human-Machine Interface 1Large Vocabulary Continuous Speech Recognition 2Text-To-Speech Synthesis 3Language Acquisition 1Acoustic Phonetics 1Speaker Adaptation 2Speech Coding 2Hidden Markov Model Techniques 2Multilingual Perception and Recognition 1Large Vocabulary Continuous Speech Recognition 3Articulatory Modelling 3Language Acquisition 2Speaker and Language Recognition 3Text-To-Speech Synthesis 4Spoken Language Understanding Systems 4Human Speech Perception 2Large Vocabulary Continuous Speech Recognition 4Spoken Language Understanding Systems 2Signal Processing and Speech Analysis 3Human Speech Perception 3Speaker Adaptation 3Spoken Language Understanding Systems 3Multimodal Spoken Language Processing 3Acoustic Phonetics 2Large Vocabulary Continuous Speech Recognition 5Speech Coding 3Language Acquisition 3 / Multilingual Perception and Recognition 2Segmentation, Labelling and Speech Corpora 3Text-To-Speech Synthesis 5Spoken Language Generation and Translation 2Human Speech Perception 4Robust Speech Processing in Adverse Environments 5Text-To-Speech Synthesis 6Speech Technology Applications and Human-Machine Interface 2Prosody and Emotion 6Hidden Markov Model Techniques 3Speech and Hearing Disorders 2 / Speech Processing for the Speech and Hearing Impaired 1Human Speech ProductionSegmentation, Labelling and Speech Corpora 4Speaker and Language Recognition 4Speech Technology Applications and Human-Machine Interface 3Utterance Verification and Word Spotting 2Large Vocabulary Continuous Speech Recognition 6Neural Networks, Fuzzy and Evolutionary Methods 3Speech Processing for the Speech-Impaired and Hearing-Impaired 2Prosody and Emotion 72: SST Student Day
SST Student Day - Poster Session 1SST Student Day - Poster Session 2Author Index
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Bilingual and Dialectal Adaptation and Retraining
Authors:
Ulla Uebler, Bavarian Research Center for Knowledge Based Systems (Germany)
Michael Schüßler, Bavarian Research Center for Knowledge Based Systems (Germany)
Heinrich Niemann, Bavarian Research Center for Knowledge Based Systems (Germany)
Page (NA) Paper number 337
Abstract:
In this paper, we report our investigations on the use of adaptation and retraining in our bilingual (Italian, German) and multidialectal recognition system. Our approach for bilingual speech recognition is to assume the two languages as being one, which is best suited for a task where Italian and German natives speak both languages, resulting in a variety of accents and dialects. We performed adaptation on single speakers and speaker groups built from combinations of spoken and native language. Furthermore, we performed retraining on partitions of the adaptation or training data. Our experiments led to an error rate reduction in all cases: compared to the baseline system, we achieved an overall improvement of 14, 12--14 and 7 % for speaker adaptation, speaker group adaptation and retraining, respectively. Furthermore, we found among others that performance is rather stable for Italian between adaptation and retraining, while adaptation for German outperforms retraining by far.
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Language Independent and Language Adaptive Large Vocabulary Speech Recognition
Authors:
Tanja Schultz, Interactive Systems Laboratories (Germany)
Alex Waibel, Interactive Systems Laboratories (USA)
Page (NA) Paper number 577
Abstract:
This paper describes the design of a multilingual speech recognizer using an LVCSR dictation database which has been collected under the project GlobalPhone. This project at the University of Karlsruhe investigates LVCSR systems in 15 languages of the world, namely Arabic, Chinese, Croatian, English, French, German, Italian, Japanese, Korean, Portuguese, Russian, Spanish, Swedish, Tamil, and Turkish. Based on a global phoneme set we built different multilingual speech recognition systems for five of the 15 languages. Context dependent phoneme models are created data-driven by introducing questions about languages and language groups to our polyphone clustering procedure. We apply the resulting multilingual models to unseen languages and present several recognition results in language independent and language adaptive setups. The results indicate that the method of parameter sharing should be decided depending on whether multilingual or crosslingual speech recognition is projected.
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A Method for Measuring the Intelligibility and Nonnativeness of Phone Quality in Foreign Language Pronunciation Training
Authors:
Goh Kawai, University of Tokyo (Japan)
Keikichi Hirose, University of Tokyo (Japan)
Page (NA) Paper number 782
Abstract:
The problem addressed is automatically detecting, measuring and correcting nonnative pronunciation characteristics (so-called "foreign accents") in foreign language speech. Systemic, structural and realizational differences between L1 (native language) and L2 (target language) appear as phone insertions, deletions and substitutions. A bilingual phone recognizer using native-trained acoustic models of the learner's L1 and L2 was developed to identify insertions, deletions and substitutions of L2 phones. Recognition results are combined with knowledge of phonetics, phonology and pedagogy to show learners which phones were mispronounced and to instruct how to modify their articulatory gestures for more native-sounding speech. The degree of the learner's foreign accent is measured based on the number of alternate pronunciations the learner uses; the number decreases as learning progresses. Evaluation experiments using Japanese and American English indicate that the system is an effective component technology for computer-aided pronunciation learning.