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This paper discusses a spoken language acquisition system for a command-and-control interface. The proposed system learns a set of words through coupled commands and demonstrations. The user can teach the system a new command by demonstrating the uttered command through an alternative interface. With these coupled commands and demonstrations, the system can learn the acoustic representations of the...
We describe a novel fast speaker adaptation algorithm for large vocabulary speech recognition systems, which adapts both the Gaussian means and the mixture weights. Gaussian means are expressed as a linear combination of eigenvoices estimated with principal component analysis. The non-negative Gaussian mixture weights are expressed as a linear combination of a set of latent vectors estimated with...
When applied to speech, Non-negative Matrix Factorization is capable of learning a small vocabulary of words, foregoing any prior linguistic knowledge. This makes it adequate for small-scale speech applications where flexibility is of the utmost importance, e.g. assistive technology for the speech impaired. However, its performance depends on the way its inputs are represented. We propose the use...
In prior work, we proposed a method for vocabulary acquisition based on a co-occurrence model and non-negative matrix factorization. The vocabulary is described in terms of co-occurrence statistics of frame-level acoustic descriptions and suffers from poor scalability to larger vocabularies. Much like whole-word HMM models, there is no reuse of a sub-word units such as phone models. In this paper,...
A speech recognition system that automatically learns word models for a small vocabulary from examples of its usage, without using prior linguistic information, can be of great use in cognitive robotics, human-machine interfaces, and assistive devices. In the latter case, the user's speech capabilities may also be affected. In this paper, we consider a NMF-based learning framework capable of doing...
In many criminal cases, evidence might be in the form of telephone conversations or tape recordings. Therefore, law enforcement agencies have been concerned about accurate methods to profile different characteristics of a speaker from recorded voice patterns, which facilitate the identification of a criminal. This paper proposes a new approach for speaker gender detection and age estimation, based...
In this paper, we describe a novel speaker adaptation algorithm based on Gaussian mixture weight adaptation. A small number of latent speaker vectors are estimated with non-negative matrix factorization (NMF). These base vectors encode the correlations between Gaussian activations as learned from the train data. Expressing the speaker dependent Gaussian mixture weights as a linear combination of a...
Computional learning from multimodal data is often done with matrix factorization techniques such as NMF (Non-negative Matrix Factorization), pLSA (Probabilistic Latent Semantic Analysis) or LDA (Latent Dirichlet Allocation). The different modalities of the input are to this end converted into features that are easily placed in a vectorized format. An inherent weakness of such a data representation...
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