A Client mobile application for Chinese-Spanish statistical machine translation

This show and tell paper describes a client mobile application for Chinese-Spanish machine translation. The system combines a standard server-based statistical machine translation (SMT) system, which requires online operation, with different input modalities including text, optical character recognition (OCR) and automatic speech recognition (ASR). It also includes an index-based search engine for supporting off-line translation.Postprint (published version

Improved Chinese Language Processing for an Open Source Search Engine

Natural Language Processing (NLP) is the process of computers analyzing on human languages. There are also many areas in NLP. Some of the areas include speech recognition, natural language understanding, and natural language generation. Information retrieval and natural language processing for Asians languages has its own unique set of challenges not present for Indo-European languages. Some of these are text segmentation, named entity recognition in unsegmented text, and part of speech tagging. In this report, we describe our implementation of and experiments with improving the Chinese language processing sub-component of an open source search engine, Yioop. In particular, we rewrote and improved the following sub-systems of Yioop to try to make them as state-of-the-art as possible: Chinese text segmentation, Part-of-speech (POS) tagging, Named Entity Recognition (NER), and Question and Answering System. Compared to the previous system we had a 9% improvement on Chinese words Segmentation accuracy. We built POS tagging with 89% accuracy. And We implement NER System with 76% accuracy

Gabor-based audiovisual fusion for Mandarin Chinese speech recognition

Audiovisual Speech Recognition (AVSR) is a popular research topic, and incorporating visual features into speech recognition systems has been found to deliver good results. In recent years, end-to-end Convolutional Neural Network (CNN) based deep learning has been widely utilized. However, these often require big data and can be time consuming to train. A lot of speech research also tends to focus on English language datasets. In this paper, we propose a lightweight optimized and automated speech recognition system using Gabor based feature extraction, combined with our Audiovisual Mandarin Chinese (AVMC) corpus. This combines Mel-frequency Cepstral Coefficients (MFCCs) + CNN_Bidirectional Long Short-term Memory (BiLSTM)_Attention (CLA) model for Audio Speech Recognition, and low dimension Gabor visual features + CLA model for Visual Speech Recognition. As we are focusing on Chinese language recognition, we individually analyse initials, finals, and tones, as part of pinyin speech production. The proposed low dimensionality system achieves 88.6%, 87.5% and 93.6% accuracy for tones, initials and finals respectively at char-level, 84.8% for pinyin at word-level

Incorporating pitch features for tone modeling in automatic recognition of Mandarin Chinese

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 53-56).Tone plays a fundamental role in Mandarin Chinese, as it plays a lexical role in determining the meanings of words in spoken Mandarin. For example, these two sentences ... (I like horses) and ... (I like to scold) differ only in the tone carried by the last syllable. Thus, the inclusion of tone-related information through analysis of pitch data should improve the performance of automatic speech recognition (ASR) systems on Mandarin Chinese. The focus of this thesis is to improve the performance of a non-tonal automatic speech recognition (ASR) system on a Mandarin Chinese corpus by implementing modifications to the system code to incorporate pitch features. We compile and format a Mandarin Chinese broadcast new corpus for use with the ASR system, and implement a pitch feature extraction algorithm. Additionally, we investigate two algorithms for incorporating pitch features in Mandarin Chinese speech recognition. Firstly, we build and test a baseline tonal ASR system with embedded tone modeling by concatenating the cepstral and pitch feature vectors for use as the input to our phonetic model (a Hidden Markov Model, or HMM). We find that our embedded tone modeling algorithm does improve performance on Mandarin Chinese, showing that including tonal information is in fact contributive for Mandarin Chinese speech recognition. Secondly, we implement and test the effectiveness of HMM-based multistream models.by Karen Lingyun Chu.M.Eng