Abstract: A computer workstation supports speech recognition software 50 and conferencing software 45, and is involved in an audio conference with one or more other workstations. Speech from the user at that workstation is transmitted to the other workstation(s), and also converted into text by the speech recognition software. The conferencing software then transmits the text to the other workstation(s). Likewise, the conferencing software also receives the text equivalent of spoken contributions from the other workstation(s). This received text, together with the locally generated text, is stored in a text file so as to produce a set of minutes for the audio conference.

Abstract: A method and system for synthesizing acoustic waveforms in, for example, a text-to-speech system is disclosed which employs the concatenation of a very large number of very small, sub-phoneme, acoustic units. Such sub-phoneme sized audio segments, called wavelets, can be individually spectrally analyzed and labelled as fenones. Fenones are clustered into logically related groups called fenemes. Sequences of fenemes can be matched with individual phonemes, and hence words. In the case of a text-to-speech system, the required phonemes are determined from prior linguistic analysis of the input words in the text. Suitable sequences of fenemes are predicted for each phoneme in its own context using hidden markov modelling techniques. A complete output waveform is constructed by concatenating wavelets to produce a very long sequence thereof, each wavelet corresponding to its respective feneme.

Abstract: The text to speech (TTS) system comprises two main components, a linguistic processor and an acoustic processor. The former is responsible for receiving an input text, and breaking it down into a sequence of phonemes. Each phoneme is assigned a duration and pitch. The acoustic processor is then responsible for reproducing the phonemes, and concatenating them into the desired acoustic output. The TTS system is driven from the output in that the linguistic processor does not operate until it receives a request from the acoustic processor for input. This request, and a return message that it can now be satisfied, are routed via a process dispatcher. By driving the system from the output, the system can be accurately halted in the event that the acoustic output needs to be interrupted.

Abstract: A speech synthesis unit comprises a text processor which breaks down text into phonemes, a prosodic processor which assigns properties such as length and pitch to the phonemes based on context, and a synthesis unit which outputs an audio signal representing the sequence of phonemes according to the specified properties. The prosodic processor includes a Hidden Markov Model (HMM) to predict the durations of the phonemes. Each state of the HMM represents a duration, and the outputs are phonemes. The HMM is trained on a set of data consisting of phonemes of known identity and duration, to allow the state transition and output distributions to be calculated. The HMM can then be used for any given input sequence of phonemes to predict a most likely sequence of corresponding durations.