Iker Arizmendi

An efficient framework for robust mobile speech recognition services

A distributed framework for implementing automatic speech recognition (ASR) services on wireless mobile devices is presented. The framework is shown to scale easily to support a large number of mobile users connected over a wireless network and degrade gracefully under peak loads. The importance of using robust acoustic modeling techniques is demonstrated for situations when the use of specialized acoustic transducers on the mobile devices is not practical. It is shown that unsupervised acoustic normalization and adaptation techniques can reduce speech recognition word error rate (WER) by 30 percent. It is also shown that an unsupervised paradigm for updating and applying these robust modeling algorithms can be efficiently implemented within the distributed framework.

Efficient client–server based implementations of mobile speech recognition services

Abstract The purpose of this paper is to demonstrate the efficiencies that can be achieved when automatic speech recognition (ASR) applications are provided to large user populations using client–server implementations of interactive voice services. It is shown that, through proper design of a client–server framework, excellent overall system performance can be obtained with minimal demands on the computing resources that are allocated to ASR. System performance is considered in the paper in terms of both ASR speed and accuracy in multi-user scenarios. An ASR resource allocation strategy is presented that maintains sub-second average speech recognition response latencies observed by users even as the number of concurrent users exceeds the available number of ASR servers by more than an order of magnitude. An architecture for unsupervised estimation of user-specific feature space adaptation and normalization algorithms is also described and evaluated. Significant reductions in ASR word error rate were obtained by applying these techniques to utterances collected from users of hand-held mobile devices. These results are important because, while there is a large body of work addressing the speed and accuracy of individual ASR decoders, there has been very little effort applied to dealing with the same issues when a large number of ASR decoders are used in multi-user scenarios.

A distributed framework for enterprise level speech recognition services

The paper presents methods for improving the efficiency of automatic speech recognition (ASR) decoders in multiuser applications. The methods involve allocating ASR resources to service human-machine dialogs in deployments that make use of many low-cost, commodity servers. It is shown that even very simple strategies for efficient allocation of ASR servers to incoming utterances has the potential to double the capacity of a multiuser deployment. This is important because, while there has been a great deal of work applied to increasing the efficiency of individual ASR engines, there has been little effort applied to increasing overall efficiency at peak loads in multiuser scenarios.