Speaker recognition

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

Audiovisual speech facilitates voice learning

Abstract: In this research, we investigated the effects of voice and face information on the perceptual learning of talkers and on long-term memory for spoken words. In the first phase, listeners were trained over several days to identify voices from words presented auditorily or audiovisually. The training data showed that visual information about speakers enhanced voice learning, revealing cross-modal connections in talker processing akin to those observed in speech processing. In the second phase, the listeners completed an auditory or audiovisual word recognition memory test in which equal numbers of words were spoken by familiar and unfamiliar talkers. The data showed that words presented by familiar talkers were more likely to be retrieved from episodic memory, regardless of modality. Together, these findings provide new information about the representational code underlying familiar talker recognition and the role of stimulus familiarity in episodic word recognition.

Adversarial Speaker Verification

Abstract: The use of deep networks to extract embeddings for speaker recognition has proven successfully. However, such embeddings are susceptible to performance degradation due to the mismatches among the training, enrollment, and test conditions. In this work, we propose an adversarial speaker verification (ASV) scheme to learn the condition-invariant deep embedding via adversarial multi-task training. In ASV, a speaker classification network and a condition identification network are jointly optimized to minimize the speaker classification loss and simultaneously mini-maximize the condition loss. The target labels of the condition network can be categorical (environment types) and continuous (SNR values). We further propose multi-factorial ASV to simultaneously suppress multiple factors that constitute the condition variability. Evaluated on a Microsoft Cortana text-dependent speaker verification task, the ASV achieves 8.8% and 14.5% relative improvements in equal error rates (EER) for known and unknown conditions, respectively.

Blind Speech Separation in a Meeting Situation with Maximum SNR Beamformers

Abstract: We propose a speech separation method for a meeting situation, where each speaker sometimes speaks and the number of speakers changes every moment. Many source separation methods have already been proposed, however, they consider a case where all the speakers keep speaking: this is not always true in a real meeting. In such cases, in addition to separation, speech detection and the classification of the detected speech according to speaker become important issues. For that purpose, we propose a method that employs a maximum signal-to-noise (MaxSNR) beamformer combined with a voice activity detector and online clustering. We also discuss the scaling ambiguity problem as regards the MaxSNR beamformer, and provide their solutions. We report some encouraging results for a real meeting in a room with a reverberation time of about 350 ms.

A Speech-and-Speaker Identification System: Feature Extraction, Description, and Classification of Speech-Signal Image

Abstract: This paper discusses a speech-and-speaker (SAS) identification system based on spoken Arabic digit recognition. The speech signals of the Arabic digits from zero to ten are processed graphically (the signal is treated as an object image for further processing). The identifying and classifying methods are performed with Burg's estimation model and the algorithm of Toeplitz matrix minimal eigenvalues as the main tools for signal-image description and feature extraction. At the stage of classification, both conventional and neural-network-based methods are used. The success rate of the speaker-identifying system obtained in the presented experiments for individually uttered words is excellent and has reached about 98.8% in some cases. The miss rate of about 1.2% was almost only because of false acceptance (13 miss cases in 1100 tested voices). These results have promisingly led to the design of a security system for SAS identification. The average overall success rate was then 97.45% in recognizing one uttered word and identifying its speaker, and 92.5% in recognizing a three-digit password (three individual words), which is really a high success rate because, for compound cases, we should successfully test all the three uttered words consecutively in addition to and after identifying their speaker; hence, the probability of making an error is basically higher. The authors' major contribution to this task involves building a system to recognize both the uttered words and their speaker through an innovative graphical algorithm for feature extraction from the voice signal. This Toeplitz-based algorithm reduces the amount of computations from operations on an ntimesn matrix that contains n2 different elements to a matrix (of Toeplitz form) that contains only n elements that are different from each other