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Speech babble is one of the most challenging noise interference for all speech systems. Here, a systematic approach to model its underlying structure is proposed to further the existing knowledge of speech processing in noisy environments. This paper establishes a working foundation for the analysis and modeling of babble speech. We first address the underlying model for multiple speaker babble speech - considering the number of conversations versus the number of speakers contributing to babble. Next, based on this model, we develop an algorithm to detect the range of the number of speakers within an unknown babble speech sequence. Evaluation is performed using 110 h of data from the Switchboard corpus. The number of simultaneous conversations ranges from one to nine, or one to 18 subjects speaking. A speaker conversation stream detection rate in excess of 80% is achieved with a speaker window size of plusmn1 speakers. Finally, the problem of in-set/out-of-set speaker recognition is considered in the context of interfering babble speech noise. Results are shown for test durations from 2-8 s, with babble speaker groups ranging from two to nine subjects. It is shown that by choosing the correct number of speakers in the background babble an overall average performance gain of 6.44% equal error rate can be obtained. This study represents effectively the first effort in developing an overall model for speech babble, and with this, contributions are made for speech system robustness in noise.
Krishnamurthy et al. (Wed,) studied this question.