Juha Iso-Sipilä

Multi-Lingual Speaker-Independent Voice User Interface For Mobile Devices

This paper presents a multi-lingual speaker-independent voice user interface (UI) that has been implemented for Nokia S60 mobile phones. The paper concentrates on discussing the specific approach used for achieving a multi-lingual and configurable speech recognition and speech synthesis system. The main applications are speaker-independent name dialing and voice commands. The novelty of the applications is that the user does not need to train the voice dialing system but the application reads the users phonebook and generates the required voice tags automatically. The speaker-independent voice dialing has already been introduced in regions where the language diversity is not so great. The system presented in this paper is the first of its kind to support both speech recognition and speech synthesis in more than 40 languages in embedded devices with strict memory and performance requirements

On a practical design of a low complexity speech recognition engine

We outline the main design features of a low complexity speech recognition engine targeted for mobile devices. Although major parts have already been presented, new features and important refinements of the original ideas, which were omitted, are now described. We also show how these techniques can be successfully combined in order to achieve various design targets with minimized impact on the recognition performance.

Fast speaker adaptation using triple diagonal and shared block diagonal transform matrices

This paper proposes two fast and effective adaptation algorithms, which are called SATD and SASBD respectively. The two algorithms are implemented in the MLLR frame and the transform matrices have constrained forms. SATD uses triple diagonal matrices to describe the mismatch between speakers and the acoustic model in the log-spectral domain and the matrices can be transformed into the cepstral domain to adjust the acoustic model. SASBD is different from the traditional block-diagonal MLLR and shares the three transformations of basic MFCC and dynamic features with one matrix. Moreover, both algorithms provide multiple choices for the biases. Experiments are extensively implemented and the results prove the advantages of SATD and SASBD over traditional MLLR.