Experiments on Detection of Voiced Hesitations in Russian Spontaneous Speech

The development and popularity of voice-user interfaces made spontaneous speech processing an important research field. One of the main focus areas in this field is automatic speech recognition (ASR) that enables the recognition and translation of spoken language into text by computers. However, ASR systems often work less efficiently for spontaneous than for read speech, since the former differs from any other type of speech in many ways. And the presence of speech disfluencies is its prominent characteristic. These phenomena are an important feature in human-human communication and at the same time they are a challenging obstacle for the speech processing tasks. In this paper we address an issue of voiced hesitations (filled pauses and sound lengthenings) detection in Russian spontaneous speech by utilizing different machine learning techniques, from grid search and gradient descent in rule-based approaches to such data-driven ones as ELM and SVM based on the automatically extracted acoustic features. Experimental results on the mixed and quality diverse corpus of spontaneous Russian speech indicate the efficiency of the techniques for the task in question, with SVM outperforming other methods

Open Source Multi-Language Audio Database for Spoken Language Processing Applications

Abstract Over the past few decades, research in automatic speech recognition and automatic speaker recognition has been greatly facilitated by the sharing of large annotated speech databases such as those distributed by the Linguistic Data Consortium (LDC). Open sources, particularly web sites such as YouTube, contain vast and varied speech recordings in a variety of languages. However, these "open sources" for speech data are largely untapped as resources for speech research. In this paper, a project to collect, organize, and annotate a large group of this speech data is described. The data consists of approximately 30 hours of speech in each of three languages, English, Mandarin Chinese, and Russian. Each of 900 recordings has been orthographically transcribed at the sentence/phrase level by human listeners. Some of the issues related to working with this low quality, varied, noisy speech data in three languages are described