MASS: A Malay language LVCSR corpus resource

2014 International Conference on Asian Language Processing (IALP)

Besacier

Lecouteux

2014

Self Cite

Abstract-Many studies have explored on the usage of existing multilingual speech corpora to build an acoustic model for a target language. These works on multilingual acoustic modeling often use multilingual acoustic models to create an initial model. This initial model created is often suboptimal in decoding speech of the target language. Some speech of the target language is then used to adapt and improve the initial model. In this paper however, we investigate multilingual acoustic modeling in enhancing an acoustic model of the target language for automatic speech recognition system. The proposed approach employs context dependent acoustic model merging of a source language to adapt acoustic model of a target language. The source and target language speech are spoken by speakers from the same country. Our experiments on Malay and English automatic speech recognition shows relative improvement in WER from 2% to about 10% when multilingual acoustic model was employed. (Abstract) automatic speech recognition; context dependent acoustic model merging; multilingual approach

Section: A Experiments Setup and Baseline Asr Resultsmentioning

confidence: 99%

Acoustic model merging using acoustic models from multilingual speakers for automatic speech recognition

2014 International Conference on Asian Language Processing (IALP)

Besacier

Lecouteux

2014

Self Cite

“…Since manual alignment of the utterances is expensive and time consuming, we applied automatic alignment by force aligning the utterances using an automatic speech recognizer (Sphinx3 from CMU). The acoustic model of the automatic speech recognizer was trained using MASS speech corpus [20] that contains about 140 hours of speech, and our pronunciation dictionary. The aligned speech is then used to train acoustic model for the HTS speech synthesis system.…”

Section: B Standard Malay Speech Synthesis Modulementioning

confidence: 99%

A Malay Dialect Translation and Synthesis System: Proposal and Preliminary System

2012 International Conference on Asian Language Processing

Seong

Khaw

2012

Self Cite

Malay is a language from the Austronesian family. Malay is the official language in Malaysia, Indonesia, Singapore, and Brunei. However, Malay spoken in different countries, and even within a country itself might vary in terms of pronunciation and vocabulary from one place to another. The Malay dialects in Malaysia can be grouped according to the states of the country. In this paper, we propose the architecture of a Malay dialect translation and synthesis system, that given a sentence in standard Malay, it translates and synthesizes an utterance in the dialect requested. The system consists of 3 modules, dialect translation system, dialect G2P system, and speech synthesis system. The outcome from this study is two folds. From linguistic viewpoint, it will help us understanding and appreciating the interesting differences in the Malay dialect in Malaysia, which is important to help preserve the dialect and culture in it. Secondly, the proposed system will be useful for people who like to learn a particular dialect or it can be used in places that require this facility. At this stage, we have completed the standard Malay system, and this paper presents our work so far.

“…Out of these, 27 are consonants, three are diphthongs and six are vowels. An IPA table for these phonemes could be found in [9]. Morphologically, Malay is an agglutinative language [10] where its words, such as verbs, are formed by adding affixes to root words.…”

Section: A Linguistic Information On Malay Languagementioning

confidence: 99%

Analysis of Malay Speech Recognition for Different Speaker Origins

Juan

Besacier

2012 International Conference on Asian Language Processing

2012

Self Cite

This paper explores speech recognition performance for Malay language with multi accents from speakers of different origins or ethnicities. Accented speech imposes accuracy problem in automatic speech recognition systems. This frequently occurs to non-native speakers of a language due to insufficiency of the non-natives data in the recognizers. In this study, we investigate the mentioned problem by building a Malay model in our recognizer and test its performance for speakers of various ethnicities. Our Malay corpora consist of read speeches and texts that are collected from local newspapers in Malaysia. Speakers who contributed the speeches are of different ethnic backgrounds. We employ context dependent models by applying linear discriminant analysis for our acoustic model and a trigram based language model. Our experiments show improved results when linear discriminant analysis technique was employed in our model while our recognizer performed worst for speakers with accent that are not available in the training data.