Artificial neural networks as a tool in ecological modelling, an introduction

Artificial neural network modeling of the river water quality—A case study

A method and apparatus for securing access to a service or facility employing automatic speech recognition, text-independent speaker identification, natural language understanding techniques and additional dynamic and static features. The method includes the steps of receiving and decoding speech containing indicia of the speaker such as a name, address or customer number; accessing a database containing information on candidate speakers; questioning the speaker based on the information; receiving, decoding and verifying an answer to the question; obtaining a voice sample of the speaker and verifying the voice sample against a model; generating a score based on the answer and the voice sample; and granting access if the score is equal to or greater than a threshold. Alternatively, the method includes the steps of receiving and decoding speech containing indicia of the speaker; generating a sub-list of speaker candidates having indicia substantially matching the speaker; activating databases containing information about the speaker candidates in the sub-list; performing voice classification analysis; eliminating speaker candidates based on the voice classification analysis; questioning the speaker regarding the information; eliminating speaker candidates based on the answer; and iteratively repeating prior steps until one speaker candidate (in which case the speaker is granted access), or no speaker candidate remains (in which case the speaker is not granted access).

Apparatus and methods for speaker verification/identification/classification employing non-acoustic and/or acoustic models and databases

A prosody analyzer enhances the interpretation of natural language utterances. The analyzer is distributed over a client/server architecture, so that the scope of emotion recognition processing tasks can be allocated on a dynamic basis based on processing resources, channel conditions, client loads etc. The partially processed prosodic data can be sent separately or combined with other speech data from the client device and streamed to a server for a real-time response. Training of the prosody analyzer with real world expected responses improves emotion modeling and the real-time identification of potential features such as emphasis, intent, attitude and semantic meaning in the speaker's utterances.

Emotion detection device and method for use in distributed systems

http://www.cse.yorku.ca/~hj/mypubs/Jiang_sc05.pdf

Confidence measures for speech recognition: A survey

A neural network is trained to transform distant-talking cepstrum coefficients, derived from a microphone array receiving speech from a speaker distant therefrom, into a form substantially similar to close-talking cepstrum coefficients that would be derived from a microphone close to the speaker, for providing robust hands-free speech and speaker recognition in adverse practical environments with existing speech and speaker recognition systems which have been trained on close-talking speech.

Method and apparatus including microphone arrays and neural networks for speech/speaker recognition systems

A long‐standing problem in the analysis and synthesis of speech by articulatory description is the estimation of the vocal tract shape parameters from natural input speech. Methods to relate spectral parameters to articulatory positions are feasible if a sufficiently large amount of data is available. This, however, results in a high computational load and large memory requirements. Further, one needs to accommodate ambiguities in this mapping due to the nonuniqueness problem (i.e., several vocal tract shapes can result in identical spectral envelopes). This paper describes the use of artificial neural networks for acoustic to articulatory parameter mapping. Experimental results show that a single feed‐forward neural net is unable to perform this mapping sufficiently well when trained on a large data set. An alternative procedure is proposed, based on an assembly of neural networks. Each network is designated to a specific region in the articulatory space, and performs a mapping from cepstral values into tract areas. The training of this assembly is executed in two stages: In the first stage, a codebook of suitably normalized articulatory parameters is used, and in the second stage, real speech data are used to further improve the mapping. During synthesis, neural networks are selected by dynamic programming using a criterion that ensures smoothly varying vocal tract shapes while maintaining a good spectral match. The method is able to accommodate nonuniqueness in acoustic‐to‐articulatory mapping and can be bootstrapped efficiently from natural speech. Results on the performance of this procedure compared to other mapping procedures, including codebook look‐up and a single multilayered network, are presented.

On the use of neural networks in articulatory speech synthesis

In many speech recognition systems, inconsistency between the training and testing conditions (i.e., effects related to noise, reverberation, microphone type, and characteristics, etc.) typically results in an unacceptable degradation in the recognition accuracy. For example, an experiment conducted in our multimedia laboratory demonstrated that the word accuracy of the Sphinx recognition system degrades from 96% to 71% when the close‐talking Senheiser microphone (CLS) at 5 in., used in training, is replaced by a hands‐free wideband line array (ARR) at 10 ft. This paper describes a neural network architecture for improving the robustness of speech recognizers in a multimedia teleconferencing environment. A multi‐layer perception (MLP) is trained to map cepstral parameters from the ARR to the CLS. An experiment conducted on three male speakers using the ARR shows that a MLP with a hidden layer of eight nodes improves the recognition accuracy of the Sphinx system from 71% to 90%. Furthermore, cross‐speaker validation (i.e., training on one speaker and testing on others) provided an 84% word accuracy. The encouraging result implies that the neural network ‘‘learns’’ the room reverberation characteristics and performs the environment adaptation largely irrespective of the speaker or the spoken text.

Robust speech recognition in a multimedia teleconferencing environment

Utterance verification represents a key technology in the design of a user-friendly speech recognition system. One essential element when designing such a system is the ability to maintain a uniform performance over a wide range of acoustic conditions. An acoustic mismatch between training and testing conditions often results in an undesirable performance degradation. This paper addresses the issue of robustness in utterance verification of a speech recognition system. Two techniques, namely signal bias removal (SBR) and on-line adaptation, are studied. The SBR algorithm is used to deal with global mismatch conditions caused by handset and channel differences. The on-line adaptation algorithm is used to adjust verification threshold at runtime for achieving a desirable trade-off between false rejection and false alarm in new test conditions. Various on-line adaptation schemes are investigated. We show that both supervised or unsupervised adaptation can effectively adjust the verification threshold to achi...

A study on robust utterance verification for connected digits recognition

An adaptive discrete cosine transform (DCT) method for image compression is presented. Clustering technique based on vector quantization (VQ) is used to reconstruct binary patterns representing the location maps of selected DCT coefficients. The DCT of image blocks are classified into broad categories and after quantization, the transform coefficients are encoded using a variable word length (VWL) coding scheme. It is demonstrated that this approach is well-suited for real-time visual communications systems, and performs efficiently at very low bit rates.

Mazin G. Rahim

Papers

Method and apparatus including microphone arrays and neural networks for speech/speaker recognition systems

On the use of neural networks in articulatory speech synthesis

Robust speech recognition in a multimedia teleconferencing environment

A study on robust utterance verification for connected digits recognition

Transform image coding using broad vector quantization