We consider methods for text-independent speaker identification that deal with the variability in the data introduced by unknown telephone channels. The methods investigated include probabilistic channel modeling, a channel-invariant model and a modified-Gaussian model. The methods are described and then evaluated with experiments conducted with a twenty speaker database of long distance telephone calls.

In this paper, we examine several methods for text-independent speaker identification of telephone speech with limited duration data, The issue addressed is the assessment of channel characteristics, especially linear aspects, and methods for improving speaker identification performance when the speaker to be identified is on a different telephone channel than that data used for training. We show experimental evidence illustrating the cross-channel problem and also show that the direct approach, of using simple channel-invariant features, can discard much speaker dependent information. The methods we have found to be most effective rely on the training process to incorporate channel variability.

In this paper, we examine several methods for text-independent speaker identification and discuss the problem of achieving robust performance. In a previous paper [1], we have described a radio-channel database that contains highly variable speech data of poor quality. Here, we describe several experiments with the radio-channel database leading to the development of robust features and methods. These experiments show that robustness to one degradation may not be sufficient to improve speaker identification accuracy. A robust feature set and modeling and classification method should mitigate the effects of many of the degradations in the data.

In this paper, we present the preliminary performance of four methods for text-independent speaker identification using speech transmitted over radio channels. In a previous paper [1], we showed that for both laboratory-quality and simulated noisy-channel data in a single-session paradigm, new probabilistic classifiers yielded performance superior to that of a minimum distance classifier. We have recently compiled a speech database consisting of speech transmissions over a radio-channel. The lower quality and higher variability of this database differ markedly from the laboratory-quality databases often used in speech processing research. We present preliminary results with the same four methods of text-independent speaker identification using the radio-channel database with several experimental paradigms including multi-session paradigms. These results show that the probabilistic methods perform significantly better than a minimum-distance classifier for the multi-session paradigm.

The feasibility of a speech coding algorithm is most effectively indicated by its successful real-time implementation. The implementation effort presents issues distinct from those related to the development of the algorithm. Problems of real-time software design and debugging, system reliability, and implementation correctness must be addressed. In addition, the power of a general purpose array processing computer may be required to accomplish the real-time aspect of the implementation. The unique, often highly parallel, architecture of available array processors can present the additional issues of multiprocessor intercommunication and control. This paper considers successful approaches to these issues in the context of two real-time speech coder algorithms implemented on an array processor, a linear predictive baseband coder operating at 9.6 kbits/s, and an adaptive predictive coder operating at 16 kbits/s.

The practical feasibility of a speech coding algorithm is most effectively indicated by its successful real-time implementation. The implementation effort presents issues distinct from those related to the development of the algorithm. Problems of real-time software design and debugging, system reliability, and implementation correctness must be addressed. In addition, the power of a special purpose array processing computer is generally required to accomplish the real-time aspect of the implementation. The unique, often highly parallel architecture of available array processors can present the additional issues of multi-processor intercommunication and control. This paper considers successful approaches to these issues in the context of two real-time speech coder algorithms implemented on an array processor (a linear predictive baseband coder operating at 9.6 kb/s, and an adaptive predictive coder operating at 16 kb/s).