Speaker recognition

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

The second ‘chime’ speech separation and recognition challenge: Datasets, tasks and baselines

Abstract: Distant-microphone automatic speech recognition (ASR) remains a challenging goal in everyday environments involving multiple background sources and reverberation. This paper is intended to be a reference on the 2nd `CHiME' Challenge, an initiative designed to analyze and evaluate the performance of ASR systems in a real-world domestic environment. Two separate tracks have been proposed: a small-vocabulary task with small speaker movements and a medium-vocabulary task without speaker movements. We discuss the rationale for the challenge and provide a detailed description of the datasets, tasks and baseline performance results for each track.

The neural integration of speaker and message

Abstract: When do listeners take into account who the speaker is? We asked people to listen to utterances whose content sometimes did not match inferences based on the identity of the speaker (e.g., If only I looked like Britney Spears in a male voice, or I have a large tattoo on my back spoken with an upper-class accent). Event-related brain responses revealed that the speaker's identity is taken into account as early as 200300 msec after the beginning of a spoken word, and is processed by the same early interpretation mechanism that constructs sentence meaning based on just the words. This finding is difficult to reconcile with standard Gricean models of sentence interpretation in which comprehenders initially compute a local, context-independent meaning for the sentence (semantics) before working out what it really means given the wider communicative context and the particular speaker (pragmatics). Because the observed brain response hinges on voice-based and usually stereotype-dependent inferences about the speaker, it also shows that listeners rapidly classify speakers on the basis of their voices and bring the associated social stereotypes to bear on what is being said. According to our event-related potential results, language comprehension takes very rapid account of the social context, and the construction of meaning based on language alone cannot be separated from the social aspects of language use. The linguistic brain relates the message to the speaker immediately.

Context-dependent modeling for acoustic-phonetic recognition of continuous speech

Abstract: This paper describes the results of our work in designing a system for phonetic recognition of unrestricted continuous speech. We describe several algorithms used to recognize phonemes using context-dependent Hidden Markov Models of the phonemes. We present results for several variations of the parameters of the algorithms. In addition, we propose a technique that makes it possible to integrate traditional acoustic-phonetic features into a hidden Markov process. The categorical decisions usually associated with heuristic acoustic-phonetic algorithms are replaced by automated training techniques and global search strategies. The combination of general spectral information and specific acoustic-phonetic features is shown to result in more accurate phonetic recognition than either representation by itself.

Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011

Abstract: Speaker emotion recognition is achieved through processing methods that include isolation of the speech signal and extraction of selected features for the final classification. In terms of acoustics, speech processing techniques offer extremely valuable paralinguistic information derived mainly from prosodic and spectral features. In some cases, the process is assisted by speech recognition systems, which contribute to the classification using linguistic information. Both frameworks deal with a very challenging problem, as emotional states do not have clear-cut boundaries and often differ from person to person. In this article, research papers that investigate emotion recognition from audio channels are surveyed and classified, based mostly on extracted and selected features and their classification methodology. Important topics from different classification techniques, such as databases available for experimentation, appropriate feature extraction and selection methods, classifiers and performance issues are discussed, with emphasis on research published in the last decade. This survey also provides a discussion on open trends, along with directions for future research on this topic.

Distributed voice recognition system

Abstract: A distributed voice recognition system includes a digital signal processor (DSP)(104), a nonvolatile storage medium (108), and a microprocessor (106). The DSP (104) is configured to extract parameters from digitized input speech samples and provide the extracted parameters to the microprocessor (106). The nonvolatile storage medium contains a database of speech templates. The microprocessor is configured to read the contents of the nonvolatile storage medium (108), compare the parameters with the contents, and select a speech template based upon the comparison. The nonvolatile storage medium may be a flash memory. The DSP (104) may be a vocoder. If the DSP (104) is a vocoder, the parameters may be diagnostic data generated by the vocoder. The distributed voice recognition system may reside on an application specific integrated circuit (ASIC).