Stefan Besling

User model-improvement-data-driven selection and update of user-oriented recognition model of a given type for word recognition at network server

A distributed pattern recognition system includes at least one user station and a server station. The server station and the user station are connected via a network, such as Internet. The server station includes different recognition models of a same type. As part of a recognition enrolment, the user station transfers model improvement data associated with a user of the user station to the server station. The server station selects a recognition model from the different recognition models of a same type in dependence on the model improvement data. For each recognition session, the user station transfers an input pattern representative of time sequential input generated by the user to the server station. The server station retrieves a recognition model selected for the user and provides the retrieved recognition model to a recognition unit for recognising the input pattern using the recognition models.

Method of speech recognition

In a method in which an information unit (4) enabling a speech input is stored on a server (5) and can be retrieved by a client (1, 2, 3) and in which the client can be coupled to one or more speech recognizers (7, 8, 9) through a communications network (6), the information unit (4) is assigned additional information (12) which is provided for determining a combination of a client (1, 2, 3) for recognizing an uttered speech signal and at least one of the speech recognizers (7, 8, 9), to dynamically assign the speech recognizers (7, 8, 9) in a communications network (6) to the information units (4) and thus ensure an acceptable processing time for the recognition of a speech input with a high recognition quality.

The Philips Research system for continuous-speech recognition

This paper gives an overview of the Philips Research system for continuous-speech recognition. The recognition architecture is based on an integrated statistical approach. The system has been successfully applied to various tasks in American English and German, ranging from small vocabulary tasks to very large vocabulary tasks and from recognition only to speech understanding. Here, we concentrate on phoneme-based continuous-speech recognition for large vocabulary recognition as used for dictation, which covers a significant part of our research work on speech recognition. We describe this task and report on experimental results. In order to allow a comparison with the performance of other systems, a section with an evaluation on the standard North American Business news (NAB2) task (dictation of American English newspaper text) is supplied.

Confidence-driven estimator perturbation: BMPC [Best Model Perturbation within Confidence]

In most practical applications of speech recognition, like for example in a dictation system, the acceptance and performance of the system depends strongly on its capability to adapt to special speaker characteristics. Restricted to the problem of language model adaptation, one has to find an efficient way to combine a typically well-trained a-priori estimator for a domain with a regularly updated but undertrained estimator reflecting the actual speaker-specific data so far. To assure a greater impact of reliable speaker-specific information, we present a new language model estimation technique that makes explicit use of the confidence in estimates obtained on the (typically small) adaptation or training data. Mathematically, it attempts to perturb a given reliable a-priori distribution in such a way that it fits into the confidence regions given by the training material. Experiments performed on real-life data supplied by US radiologists indicate that the method could improve standard adaptation techniques like linear interpolation.