Dealing with untranscribed speech

With the advent of social networks, there has been an exponential growth in multimedia data including speech. This speech data is typically conversational, casual and recorded in real environment. An important characteristic of this speech data is unavailability of corresponding transcripts (text) or the language information. In this work, we discuss technologies dealing with speech data without any corresponding transcripts and/or language information. A traditional way is to adopt acoustic models from existing benchmark databases (of known languages) for obtaining a first-level transcription and then perform bootstrapping. We show inherent limitations of such approaches, and argue that signal processing algorithms based on speech production knowledge play an important role in dealing with such speech data. This paper discusses some of the ongoing work at our lab in this direction which includes building audio search, speech summarization, speech synthesis and voice conversion using untranscribed speech.