Low Footprint High Intelligibility Malay Speech Synthesizer Based on Statistical Data

Yong, Lau Chee; Swee, Tan Tian

doi:10.3844/jcssp.2014.316.324

Cited by 3 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The studio data was obtained as described in Yong and Swee (2014). The recording script was handcrafted: starting from a word list (from a pre-existing lexicon), text covering this vocabulary was gathered from various sources such as online news in Malay and a Malay language textbook.…”

Section: Speech Data Collectionmentioning

confidence: 99%

Combining Lightly-supervised Learning and User Feedback to Construct Andimprove a Statistical Parametric Speech Synthesizer for Malay

Yong¹,

Watts²,

King

2015

RJASET

View full text Add to dashboard Cite

Abstract:In this study, we aim to reduce the human effort in preparing training data for synthesizing human speech and improve the quality of synthetic speech. In spite of the learning-from-data used to train the statistical models, the construction of a statistical parametric speech synthesizer involves substantial human effort, especially when using imperfect data or working on a new language. Here, we use lightly-supervised methods for preparing the data and constructing the text-processing front end. This initial system is then iteratively improved using active learning in which feedback from users is used to disambiguate the pronunciation system in our chosen language, Malay. The data are prepared using speaker diarisation and lightly-supervised text-speech alignment. In the front end, graphemebased units are used. The active learning used small amounts of feedback from a listener to train a classifier. We report evaluations of two systems built from high-quality studio data and lower-quality `found' data respectively and show that the intelligibility of each can be improved using active learning.

show abstract

Section: Speech Data Collectionmentioning

confidence: 99%

Combining Lightly-supervised Learning and User Feedback to Construct Andimprove a Statistical Parametric Speech Synthesizer for Malay

Yong¹,

Watts²,

King

2015

RJASET

View full text Add to dashboard Cite

show abstract

“…Malay language or Bahasa Melayu is the official language in Malaysia, Indonesia and Brunei (Yong and Swee, 2014a). There are about 500 million of Malay speakers existed in the world (Noraini and Kamaruzaman, 2008).…”

Section: Malay Language Overviewmentioning

confidence: 99%

“…Speech is a type of communication method between people (Yong and Swee, 2014b), which people design the content of it to deliver their message. The process of speech sound production is called articulation, it is caused by the movement of lips, tongue, velum and jaws to shape the flow of air into sounds (Thomas and Carmack, 1990).…”

Section: Introductionmentioning

confidence: 99%

Statistical Parametric Evaluation on New Corpus Design for Malay Speech Articulation Disorder Early Diagnosis

Mazenan¹,

Swee²,

Tan³

et al. 2015

American Journal of Applied Sciences

View full text Add to dashboard Cite

Speech-to-Text or always been known as speech recognition plays an important role nowadays especially in medical area specifically in speech impairment. In this study, a Malay language speech-to-Text system was been designed by using Hidden Markov Model (HMM) as a statistical engine with emphasizing the way of Malay speech corpus design specifically for Malay articulation speech disorder. This study also describes and tests the correct number of state to analyze the changes in the performance of current Malay speech recognition in term of recognition accuracy. Statistical parametric representation method was utilized in this study and the Malay corpus database was constructed to be balanced with all the phonetic placed and manner of articulation sample appeared in Malay speech articulation therapy. The results were achieved by conducting few experiments by collecting sample from 80 patient speakers (child and adult) and contain for almost 30,720 sample training data.

show abstract

Statistical Parametric Speech Synthesis of Malay Language using Found Training Data

Yong¹,

Swee²

2014

RJASET

View full text Add to dashboard Cite

The preparation of training data for statistical parametric speech synthesis can be sophisticated. To ensure the good quality of synthetic speech, high quality low noise recording must be prepared. The preparation of recording script can be also tremendous from words collection, words selection and sentences design. It requires tremendous human effort and takes a lot of time. In this study, we used alternative free source of recording and text such as audio-book, clean speech and so on as the training data. Some of the free source can provide high quality recording with low noise which is suitable to become training data. Statistical parametric speech synthesis method applying Hidden Markov Model (HMM) has been used. To test the reliability of synthetic speech, perceptual test has been conducted. The result of naturalness test is fairly reasonable. The intelligibility test showed encouraging result. The Word Error Rate (WER) for normal synthetic sentences is below 15% while for Semantically Unpredictable Sentences (SUS) is averagely in 30%. In short, using free and ready source as training data can leverage the process of preparing training data while obtaining motivating synthetic result.

show abstract

Low Footprint High Intelligibility Malay Speech Synthesizer Based on Statistical Data

Cited by 3 publications

References 21 publications

Combining Lightly-supervised Learning and User Feedback to Construct Andimprove a Statistical Parametric Speech Synthesizer for Malay

Combining Lightly-supervised Learning and User Feedback to Construct Andimprove a Statistical Parametric Speech Synthesizer for Malay

Statistical Parametric Evaluation on New Corpus Design for Malay Speech Articulation Disorder Early Diagnosis

Statistical Parametric Speech Synthesis of Malay Language using Found Training Data

Contact Info

Product

Resources

About