Multilingual phone models for vocabulary-independent speech recognition tasks
Speech Communication
Language-independent and language-adaptive acoustic modeling for speech recognition
Speech Communication
Development of Dialect-Specific Speech Recognizers Using Adaptation Methods
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Automatic dialect identification of extemporaneous conversational, Latin American Spanish speech
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
Towards language independent acoustic modeling
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
Multi-accent acoustic modelling of South African English
Speech Communication
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During the last years, language resources for speech recognition have been collected for many languages and specifically, for global languages. One of the characteristics of global languages is their wide geographical dispersion, and consequently, their wide phonetic, lexical, and semantic dialectal variability. Even if the collected data is huge, it is difficult to represent dialectal variants accurately. This paper deals with multidialectal acoustic modeling for Spanish. The goal is to create a set of multidialectal acoustic models that represents the sounds of the Spanish language as spoken in Latin America and Spain. A comparative study of different methods for combining data between dialects is presented. The developed approaches are based on decision tree clustering algorithms. They differ on whether a multidialectal phone set is defined, and in the decision tree structure applied. Besides, a common overall phonetic transcription for all dialects is proposed. This transcription can be used in combination with all the proposed acoustic modeling approaches. Overall transcription combined with approaches based on defining a multidialectal phone set leads to a full dialect-independent recognizer, capable to recognize any dialect even with a total absence of training data from such dialect. Multidialectal systems are evaluated over data collected in five different countries: Spain, Colombia, Venezuela, Argentina and Mexico. The best results given by multidialectal systems show a relative improvement of 13% over the results obtained with monodialectal systems. Experiments with dialect-independent systems have been conducted to recognize speech from Chile, a dialect not seen in the training process. The recognition results obtained for this dialect are similar to the ones obtained for other dialects.