This paper introduces a new method to extract speaker embeddings from a deep neural network (DNN) for text-independent speaker verification. Usually, speaker embeddings are extracted from a speaker-classification DNN that averages the hidden vectors over the frames of a speaker; the hidden vectors produced from all the frames are assumed to be equally important. We relax this assumption and compute the speaker embedding as a weighted average of a speaker's frame-level hidden vectors, and their weights are automatically determined by a self-attention mechanism. The effect of multiple attention heads are also investigated to capture different aspects of a speaker's input speech. Finally, a PLDA classifier is used to compare pairs of embeddings. The proposed self-attentive speaker embedding system is compared with a strong DNN embedding baseline on NIST SRE 2016. We find that the self-attentive embeddings achieve superior performance. Moreover, the improvement produced by the self-attentive speaker embeddings is consistent with both short and long testing utterances.

No abstract

Three perceptual experiments were conducted to test the relative importance of vowels versus consonants to recognition of fluent speech. Sentences were selected from the TIMIT corpus to obtain approximately equal numbers of vowels and consonants within each sentence and equal durations across the set of sentences. In experiments 1 and 2, subjects listened to (a) unaltered TIMIT sentences, (b) sentences in which all of the vowels were replaced by noise, or (c) sentences in which all of the consonants were replaced by noise. The subjects listened to each sentence five times, and attempted to transcribe what they heard. The results of these experiments show that recognition of words depends more upon vowels than consonants—about twice as many words are recognized when vowels are retained in the speech. The effect was observed when occurrences of [l], [r], [w], [y] [m], and [n] were included in the sentences (experiment 1) or replaced by noise (experiment 2). Experiment 3 tested the hypothesis that vowel boundaries contain more information about the neighboring consonants than vice versa.

If it is the author's pre-published version, changes introduced as a result of publishing processes such as copy-editing and formatting may not be reflected in this document. For a definitive version of this work, please refer to the published version.

No abstract

Eigenvoice-based methods have been shown to be effective for fast speaker adaptation when only a small amount of adaptation data, say, less than 10 seconds, is available. At the heart of the method is principal component analysis (PCA) employed to find the most important eigenvoices. In this paper, we postulate that nonlinear PCA using kernel methods may be even more effective. The eigenvoices thus derived will be called kernel eigenvoices (KEV), and we will call our new adaptation method kernel eigenvoice speaker adaptation. However, unlike the standard eigenvoice (EV) method, an adapted speaker model found by the kernel eigenvoice method resides in the high-dimensional kernel-induced feature space, which, in general, cannot be mapped back to an exact pre-image in the input speaker supervector space. Consequently, it is not clear how to obtain the constituent Gaussians of the adapted model that are needed for the computation of state observation likelihoods during the estimation of eigenvoice weights and subsequent decoding. Our solution is the use of composite kernels in such a way that state observation likelihoods can be computed using only kernel functions without the need of a speaker-adapted model in the input supervector space. In this paper, we investigate two different composite kernels for KEV adaptation: direct sum kernel and tensor product kernel. In an evaluation on the TIDIGITS task, it is found that KEV speaker adaptation using both forms of composite Gaussian kernels are equally effective, and they outperform a speaker-independent model and adapted models found by EV, MAP, or MLLR adaptation using 2.1s and 4.1s of speech. For example, with 2.1s of adaptation data, KEV adaptation outperforms the speaker-independent model by 27.5%, whereas EV, MAP, or MLLR adaptation are not effective at all. Keywords Kernel eigenvoice speaker adaptation, eigenvoice speaker adaptation, kernel principal component analysis, composite kernels, generalized EM algorithm EDICS Category: 1-RECO

No abstract

No abstract