AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline

An open-source Mandarin speech corpus called AISHELL-1 is released. It is by far the largest corpus which is suitable for conducting the speech recognition research and building speech recognition systems for Mandarin. The recording procedure, including audio capturing devices and environments are presented in details. The preparation of the related resources, including transcriptions and lexicon are described. The corpus is released with a Kaldi recipe. Experimental results implies that the quality of audio recordings and transcriptions are promising.Comment: Oriental COCOSDA 201

Exploiting Contextual Information for Prosodic Event Detection Using Auto-Context

Prosody and prosodic boundaries carry significant information regarding linguistics and paralinguistics and are important aspects of speech. In the field of prosodic event detection, many local acoustic features have been investigated; however, contextual information has not yet been thoroughly exploited. The most difficult aspect of this lies in learning the long-distance contextual dependencies effectively and efficiently. To address this problem, we introduce the use of an algorithm called auto-context. In this algorithm, a classifier is first trained based on a set of local acoustic features, after which the generated probabilities are used along with the local features as contextual information to train new classifiers. By iteratively using updated probabilities as the contextual information, the algorithm can accurately model contextual dependencies and improve classification ability. The advantages of this method include its flexible structure and the ability of capturing contextual relationships. When using the auto-context algorithm based on support vector machine, we can improve the detection accuracy by about 3% and F-score by more than 7% on both two-way and four-way pitch accent detections in combination with the acoustic context. For boundary detection, the accuracy improvement is about 1% and the F-score improvement reaches 12%. The new algorithm outperforms conditional random fields, especially on boundary detection in terms of F-score. It also outperforms an n-gram language model on the task of pitch accent detection

Facing the Mirror: Dilemmas and Issues Encountered on a TESOL programme in an International University Environment

Abstract: This paper investigates the experiences of three postgraduate students studying on an MA TESOL and Applied Linguistics course in a British university context. It demonstrates how subtle discourses of „ownership‟ of English (Holliday, 2014; Pennycook, 1994, 2001; Kumaravadevelu, 2003) persist in such training contexts, despite the general shift towards internationalizing higher education environments in the UK. The paper will discuss how the participants negotiated the teaching practice components of the course, and the issues they faced through being „non-native‟ speakers of English. It further examines the impact this had on their professional development and self-perceptions of „legitimacy‟ as teachers of English. The different constructs of a TESOL teacher are discussed and the need for a heightened awareness of training needs for teachers across diverse contexts

Speech intelligibility in multilingual spaces

No abstract available