Speech and crosstalk detection in multichannel audio

The analysis of scenarios in which a number of microphones record the activity of speakers, such as in a round-table meeting, presents a number of computational challenges. For example, if each participant wears a microphone, speech from both the microphone's wearer (local speech) and from other participants (crosstalk) is received. The recorded audio can be broadly classified in four ways: local speech, crosstalk plus local speech, crosstalk alone and silence. We describe two experiments related to the automatic classification of audio into these four classes. The first experiment attempted to optimize a set of acoustic features for use with a Gaussian mixture model (GMM) classifier. A large set of potential acoustic features were considered, some of which have been employed in previous studies. The best-performing features were found to be kurtosis, "fundamentalness," and cross-correlation metrics. The second experiment used these features to train an ergodic hidden Markov model classifier. Tests performed on a large corpus of recorded meetings show classification accuracies of up to 96%, and automatic speech recognition performance close to that obtained using ground truth segmentation

Innovative Method for Unsupervised Voice Activity Detection and Classification of Audio Segments

An accurate and noise-robust voice activity detection (VAD) system can be widely used for emerging speech technologies in the fields of audio forensics, wireless communication, and speech recognition. However, in real-life application, the sufficient amount of data or human-annotated data to train such a system may not be available. Therefore, a supervised system for VAD cannot be used in such situations. In this paper, an unsupervised method for VAD is proposed to label the segments of speech-presence and speech-absence in an audio. To make the proposed method efficient and computationally fast, it is implemented by using long-term features that are computed by using the Katz algorithm of fractal dimension estimation. Two databases of different languages are used to evaluate the performance of the proposed method. The first is Texas Instruments Massachusetts Institute of Technology (TIMIT) database, and the second is the King Saud University (KSU) Arabic speech database. The language of TIMIT is English, while the language of the KSU speech database is Arabic. TIMIT is recorded in only one environment, whereas the KSU speech database is recorded in distinct environments using various recording systems that contain sound cards of different qualities and models. The evaluation of the proposed method suggested that it labels voiced and unvoiced segments reliably in both clean and noisy audio

Measuring Mimicry in Task-Oriented Conversations: The More the Task is Difficult, The More we Mimick our Interlocutors

The tendency to unconsciously imitate others in conversations is referred to as mimicry, accommodation, interpersonal adap- tation, etc. During the last years, the computing community has made significant efforts towards the automatic detection of the phenomenon, but a widely accepted approach is still miss- ing. Given that mimicry is the unconscious tendency to imitate others, this article proposes the adoption of speaker verification methodologies that were originally conceived to spot people trying to forge the voice of others. Preliminary experiments suggest that mimicry can be detected by measuring how much speakers converge or diverge with respect to one another in terms of acoustic evidence. As a validation of the approach, the experiments show that convergence (the speakers become more similar in terms of acoustic properties) tends to appear more frequently when a task is difficult and, therefore, requires more time to be addressed