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Abstract: The Intractable Speakers Recognition (ISR) speech database with a vocabulary of 11000 words has become very popular in many speech related tasks. However, in ISR the number of phonemes is much smaller than the number of words, which potentially reduces the recognition or classification accuracy because of the sparsity problem of phonemes. In this paper, quasi-phoneme Viterbi decoding, biphone-based acoustic modeling and distance match are introduced in the proposed algorithms to improve the performance of ISR involving small vocabulary tasks. Both semi-supervised and unsupervised learning methods are employed to augment the training speech data with existing speech databases, and the results show that the proposed methods without human judgements give a relative improvement of about 6% in terms of F-score, when compared to unsupervised learning methods. This is a significant progress in the area of phoneme recognition with a small vocabulary, and has important implications for the development of relevant machine learning algorithms and their applications.

Intuitively, the nearest neighbor principle that points can be assumed to be sampled from the surface of the local neighborhood graph of a point after some translation should always be elementary. However, such a feature is not unique; for example, it can be described as an invariance idea, where points can be assumed to have been sampled from the surface of the neighborhood of a point after a multiplication of the point coordinates by a unitary translation. We will talk about this idea in this paper. Note that when we talk about the nearest neighbors of a point, we mean the points lying on the surface of the neighborhood of this point.

Assume that a point with coordinates xi is sampled from the surface of a neighborhood of a point with coordinates xj, This means that xi = f(xj), which means that xi = a(xj)xj, where a(xj) = f(xj) depends on the neighborhood. Note that we assume that d(xj, xi) = 0; if this is not the case, the nearest neighbors corresponding to the point xj are those points which correspond to the distance d(xj, yi) = 0.

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