Abstract: A method is disclosed for providing speech parameters to be used for synthesis of a speech utterance. In at least one embodiment, the method includes receiving an input time series of first speech parameter vectors, preparing at least one input time series of second speech parameter vectors consisting of dynamic speech parameters, extracting from the input time series of first and second speech parameter vectors partial time series of first speech parameter vectors and corresponding partial time series of second speech parameter vectors, converting the corresponding partial time series of first and second speech parameter vectors into partial time series of third speech parameter vectors, wherein the conversion is done independently for each set of partial time series and can be started as soon as the vectors of the input time series of the first speech parameter vectors have been received.

Abstract: The method provides a spectral speech description to be used for synthesis of a speech utterance, where at least one spectral envelope input representation is received. In one solution the improvement is made by manipulation an extremum, i.e. a peak or a valley, in the rapidly varying component of the spectral envelope representation. The rapidly varying component of the spectral envelope representation is manipulated to sharpen and/or accentuate extrema after which it is merged back with the slowly varying component or the spectral envelope input representation to create an enhanced spectral envelope final representation. In other solutions a complex spectrum envelope final representation is created with phase information derived from one of the group delay representation of a real spectral envelope input representation corresponding to a short-time speech signal and a transformed phase component of the discrete complex frequency domain input representation corresponding to the speech utterance.

Abstract: In many application environments, it is desirable to provide voice access to tables on Internet pages, where the user asks a subject-related question in a natural language and receives an adequate answer from the table read out to him in a natural language. A method is disclosed for preparing information presented in a tabular form for a speech dialogue system so that the information of the table can be consulted in a user dialogue in a targeted manner.

Abstract: An input linguistic description is converted into a speech waveform by deriving at least one target unit sequence corresponding to the linguistic description, selecting from a waveform unit database for the target unit sequences a plurality of alternative unit sequences approximating the target unit sequences, concatenating the alternative unit sequences to alternative speech waveforms and presenting the alternative speech waveforms to an operating person and enabling the choice of one of the presented alternative speech waveforms. There are no iterative cycles of manual modification and automatic selection, which enables a fast way of working. The operator does not need knowledge of units, targets, and costs, but chooses from a set of given alternatives. The fine-tuning of TTS prompts therefore becomes accessible to non-experts.

Abstract: Methods and apparatus for speech recognition based on a hidden Markov model are disclosed. A disclosed method of speech recognition is based on a hidden Markov model in which words to be recognized are modeled as chains of states and trained using predefined speech data material. Known vocabulary is divided into first and second partial vocabularies where the first partial vocabulary is trained and transcribed using a whole word model and the second partial vocabulary is trained and transcribed using a phoneme-based model in order to obtain a mixed hidden Markov model. The transcriptions from the two models are stored in a single pronunciation lexicon and the mixed hidden Markov model stored in a singe search space. Apparatus are disclosed that also employ a hidden Markov model.

Abstract: The voice of a synthesized voice output is individualized and matched to a user voice, the voice of a communication partner or the voice of a famous personality. In this way mobile terminals in particular can be originally individualized and text messages can be read out using a specific voice.

Abstract: Speaker-dependent speech recognition is performed upon detecting a speech signal encompassing a voice command. The speech signal is divided into time frames and characterized in each detected time frame by forming a corresponding property vector. A language-independent feature vector sequence is formed from one or several property vectors and then stored. The language-independent feature vector sequence is allocated to a language-dependent sequence of model vectors in a speech resource having a plurality of model vectors. A piece of allocation information indicating allocation of the language-independent feature vector sequence to a language-dependent sequence of model vectors is stored, then the voice command allocated to the model vector sequence is identified.