Speaker recognition

About: Speaker recognition is a research topic. Over the lifetime, 14990 publications have been published within this topic receiving 310061 citations.

Text-dependent speaker verification

Abstract: A text-dependent speaker verification technique that uses a generic speaker-independent speech recognizer for robust speaker verification, and uses the acoustical model of a speaker-independent speech recognizer as a background model. Instead of using a likelihood ratio test (LRT) at the utterance level (e.g., the sentence level), which is typical of most speaker verification systems, the present text-dependent speaker verification technique uses weighted sum of likelihood ratios at the sub-unit level (word, tri-phone, or phone) as well as at the utterance level.

Systems and methods for automatic acoustic speaker adaptation in computer-assisted transcription systems

Abstract: The invention is a system and method for automatic acoustic speaker adaptation in an automatic speech recognition assisted transcription system. Partial transcripts of audio files are generated by a transcriptionist. A topic language model is generated from the partial transcripts. The topic language model is interpolated with a general language model. Automatic speech recognition is performed on the audio files by a speech recognition engine using a speaker independent acoustic model and the interpolated language model to generate semi-literal transcripts of the audio files. The semi-literal transcripts are then used with the corresponding audio files to generate a speaker dependent acoustic model in an acoustic adaptation engine.

Fusion of audio and video based speaker identification for multimedia information access

Abstract: A method and apparatus are disclosed for identifying a speaker in an audio-video source using both audio and video information. An audio-based speaker identification system identifies one or more potential speakers for a given segment using an enrolled speaker database. A video-based speaker identification system identifies one or more potential speakers for a given segment using a face detector/recognizer and an enrolled face database. An audio-video decision fusion process evaluates the individuals identified by the audio-based and video-based speaker identification systems and determines the speaker of an utterance in accordance with the present invention. A linear variation is imposed on the ranked-lists produced using the audio and video information. The decision fusion scheme of the present invention is based on a linear combination of the audio and the video ranked-lists. The line with the higher slope is assumed to convey more discriminative information. The normalized slopes of the two lines are used as the weight of the respective results when combining the scores from the audio-based and video-based speaker analysis. In this manner, the weights are derived from the data itself.

Comparison of Speaker Recognition Approaches for Real Applications

Abstract: This paper describes the experimental setup and the results obtained using several state-of-the-art speaker recognition classifiers. The comparison of the different approaches aims at the development of real world applications, taking into account memory and computational constraints, and possible mismatches with respect to the training environment. The NIST SRE 2008 database has been considered our reference dataset, whereas nine commercially available databases of conversational speech in languages different form the ones used for developing the speaker recognition systems have been tested as representative of an application domain. Our results, evaluated on the two domains, show that the classifiers based on i-vectors obtain the best recognition and calibration accuracy. Gaussian PLDA and a recently introduced discriminative SVM together with an adaptive symmetric score normalization achieve the best performance using low memory and processing resources. Index Terms: Speaker Recognition, i-vectors, Joint Factor Analysis, Support Vector Machines

Prosodic and other Long-Term Features for Speaker Diarization

Abstract: Speaker diarization is defined as the task of determining ldquowho spoke whenrdquo given an audio track and no other prior knowledge of any kind. The following article shows how a state-of-the-art speaker diarization system can be improved by combining traditional short-term features (MFCCs) with prosodic and other long-term features. First, we present a framework to study the speaker discriminability of 70 different long-term features. Then, we show how the top-ranked long-term features can be combined with short-term features to increase the accuracy of speaker diarization. The results were measured on standardized datasets (NIST RT) and show a consistent improvement of about 30% relative in diarization error rate compared to the best system presented at the NIST evaluation in 2007.