Jan Sedivy

The IBM Personal Speech Assistant

We describe technology and experience with an experimental personal information manager, which interacts with the user primarily but not exclusively through speech recognition and synthesis. This device, which controls a client PDA, is known as the personal speech assistant (PSA). The PSA contains complete speech recognition, speech synthesis and dialog management systems. Packaged in a hand-sized enclosure, of size and physical design to mate with the popular Palm III personal digital assistant, the PSA includes its own battery, microphone, speaker, audio input and output amplifiers, processor and memory. The PSA supports speaker-independent English speech recognition using a 500-word vocabulary, and English speech synthesis on an arbitrary vocabulary. We survey the technical issues we encountered in building the hardware and software for this device, and the solutions we implemented, including audio system design, power and space budget, speech recognition in adverse acoustic environments with constrained processing resources, dialog management, appealing applications, and overall system architecture.

Two-pass search strategy for large list recognition on embedded speech recognition platforms

This paper presents an efficient algorithm for a speech recognition system which can process large lists of items. The described two-pass search implementation focuses on maximizing the speed and minimizing the memory footprint of the search engine. The algorithm is designed to handle thousands or tens of thousands of words in a search space restricted by a grammar. A typical example of such a task is stock name recognition, street name finding, song selection etc. The intended application of this algorithm is in embedded ASR system in portable devices (e.g. iPAQ) or cars.

Speech coding apparatus and method for generating acoustic feature vector component values by combining values of the same features for multiple time intervals

A speech coding apparatus and method measures the values of at least first and second different features of an utterance during each of a series of successive time intervals. For each time interval, a feature vector signal has a first component value equal to a first weighted combination of the values of only one feature of the utterance for at least two time intervals. The feature vector signal has a second component value equal to a second weighted combination, different from the first weighted combination, of the values of only one feature of the utterance for at least two time intervals. The resulting feature vector signals for a series of successive time intervals form a coded representation of the utterance. In one embodiment, a first weighted mixture signal has a value equal to a first weighted mixture of the values of the features of the utterance during a single time interval. A second weighted mixture signal has a value equal to a second weighted mixture, different from the first weighted mixture, of the values of the features of the utterance during a single time interval. The first component value of each feature vector signal is equal to a first weighted combination of the values of only the first weighted mixture signals for at least two time intervals, and the second component value of each feature vector signal is equal to a second weighted combination, different from the first weighted combination, of the values of only the second weighted mixture for at least two time intervals.