Exploring CTC Based End-to-End Techniques for Myanmar Speech Recognition
In this work, we explore a Connectionist Temporal Classification (CTC) based end-to-end Automatic Speech Recognition (ASR) model for the Myanmar language. A series of experiments is presented on the topology of the model in which the convolutional layers are added and dropped, different depths of bidirectional long short-term memory (BLSTM) layers are used and different label encoding methods are investigated. The experiments are carried out in low-resource scenarios using our recorded Myanmar speech corpus of nearly 26 hours. The best model achieves character error rate (CER) of 4.72% and syllable error rate (SER) of 12.38% on the test set.
Code (0)
등록된 구현이 없습니다.
Tasks
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)speech-recognitionSpeech RecognitionSimilar Papers 제목 키워드 기반
Comparison of Grapheme-to-Phoneme Conversion Methods on a Myanmar Pronunciation Dictionary
Grapheme-to-Phoneme (G2P) conversion is the task of predicting the pronunciation of a word given its graphemic or written form. It is a highly important part of both automatic speech recognition (ASR) and text-to-speech …
Active LearningAutomatic Speech RecognitionAutomatic Speech Recognition (ASR)Grapheme-to-Phoneme Conversion+6Syllable-based Neural Named Entity Recognition for Myanmar Language
Named Entity Recognition (NER) for Myanmar Language is essential to Myanmar natural language processing research work. In this work, NER for Myanmar language is treated as a sequence tagging problem and the effectiveness…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NERIntroducing the Asian Language Treebank (ALT)
This paper introduces the ALT project initiated by the Advanced Speech Translation Research and Development Promotion Center (ASTREC), NICT, Kyoto, Japan. The aim of this project is to accelerate NLP research for Asian l…
ArticlesSentenceTranslationChina, India, Myanmar: Playing Rohingya Roulette
The 2017 crackdown on Rakhine Rohingyas by the Myanmar army (Tatmadaw) pushed more than 600,000 refugees into Bangladesh. Both Western and Islamic countries denounced Aung Sang Suu Kyis government, but both Asian giants,…
Exploring End-to-End Techniques for Low-Resource Speech Recognition
In this work we present simple grapheme-based system for low-resource speech recognition using Babel data for Turkish spontaneous speech (80 hours). We have investigated different neural network architectures performance…
speech-recognitionSpeech Recognition