Language diarization for code-switch conversational speech

This paper examines the process of language diarization, the process to perform language segmentation and recognition, in a code-switched speech. Towards this task, we have developed a 63 hours conversational code-switch corpus recorded from Singapore/Malaysia speakers. We show that code-switching can occur frequently and the average language interval may be as short as one second. As such, language diarization is challenging task. To process such short segments, we propose a language diarization system using long term context feature across several phone-based segments and the combination of acoustics and phonotactic information. We achieved a frame error rate of 14.7% for language diarization performance on a Mandarin-English code-switch corpus. To evaluate our system, we measured the language recognition performance on monolingual segments extracted from the code-switch corpus against published techniques of LID systems - we obtained a relative equal error rate reduction of 5.2%, 13.8%, 15.1% and 17.9% on speech durations of 0.1 to 0.5 sec., 0.5 to 1 sec., 1 to 3 sec. and 3 to 9 sec respectively.