In this paper the phoneme errors in adaptation data of HMM based synthesis is investigated. Phoneme errors are likely to appear in automatic speech recognition (ASR) based transcriptions. The research also investigates the perspective of merely ASR transcription based unsupervised adaptation. To achieve better quality a new method is introduced for selecting an optimal subset of ASR transcription based adaptation data. Quality evaluation of the method was also performed. The results showed that adaptation was successful even on higher than 50% phoneme error rates.

Various morphological and acoustic modeling techniques are evaluated on a less resourced, spontaneous Hungarian large-vocabulary continuous speech recognition (LVCSR) task. Among morphologically rich languages, Hungarian is known for its agglutinative, inflective nature that increases the data sparseness caused by a relatively small training database. Although Hungarian spelling is considered as simple phonological, a large part of the corpus is covered by words pronounced in multiple, phonemically different ways. Data-driven and language specific knowledge supported vocabulary decomposition methods are investigated in combination with phoneme- and grapheme-based acoustic modeling techniques on the given task. Word baseline and morph-based advanced baseline results are significantly outperformed by using both statistical and grammatical vocabulary decomposition methods. Although the discussed morph-based techniques recognize a significant amount of out of vocabulary words, the improvements are due not to this fact but to the reduction of insertion errors. Applying grapheme-based acoustic models instead of phoneme-based models causes no severe recognition performance deteriorations. Moreover, a fully data-driven acoustic modeling technique along with a statistical morphological modeling approach provides the best performance on the most difficult test set. The overall best speech recognition performance is obtained by using a novel word to morph decomposition technique that combines grammatical and unsupervised statistical segmentation algorithms. The improvement achieved by the proposed technique is stable across acoustic modeling approaches and larger with speaker adaptation.

A coupled acoustic- and language-modeling approach is presented for the recognition of spontaneous speech primarily in agglutinative languages. The effectiveness of the approach in large vocabulary spontaneous speech recognition is demonstrated on the Hungarian MALACH corpus. The derivation of morphs from word forms is based on a statistical morphological segmentation tool while the mapping of morphs into graphemes is obtained trivially by splitting each morph into individual letters. Using morphs instead of words in language modeling gives significant WER reductions in case of both phoneme- and grapheme-based acoustic modeling. The improvements are larger after speaker adaptation of the acoustic models. In conclusion, morphophonemic and the proposed morpho-graphemic ASR approaches yield the same best WERs, which are significantly lower than the word-based baselines but essentially without language dependent rules or pronunciation dictionaries in the latter case. Index Terms: spontaneous speech recognition, morphology.

A novel noise robust voice activity detection approach is introduced. The novelty of the method that it uses noise suppressed spectrum of the input signal for spectral entropy calculation. As a result excellent end-pointing performance is observed based on predefined global entropy threshold and time constraints. The effect of frame dropping controlled by the proposed algorithm was investigated on the accuracy of automatic speech recognition. The experiments were performed on Hungarian publicly available noisy and normal telephony speech databases. The relative improvement due to dropping of non-speech frames was positive in all test configurations with a maximum of 29,5%. Besides, in average more than 50% of the frames were dropped.

Abstract In this article we present a voice enabled call center whichintegrates our basic and applied research results on Hungarianspeech recognition. Telephone interfaces, data storage and re-trieval modules, and an intelligent dialog descriptor and man-ager module are also parts of the system. To evaluate the ef-ficiency of the recognition and the dialog, a voice enabled callcenter was implemented and tested under real life conditions.This article describes the main modules of the system and com-pares the result of the field tests with that of the laboratory test-ing. 1. Introduction Hungarian speech research has a long tradition at our Labora-tory, there exist some research systems both for speech synthe-sis and for ASR (automatic speech recognition). Our previousASR research results were focused on special problems, likeacoustic model training, language modeling [1], and pronuncia-tion modeling [3]. Recently, large Hungarian speech databaseshave become available, recorded over mobile and fixed tele-phone channels, which encouraged the implementation of di-alog systems that can be used for various services in the field oftelephony. On one hand we had to integrate our previous basicand applied research results while, on the other hand, applyingreal voice driven dialogs initiated a new research area for Hun-garian.In the following section we provide an overview of thecall center realized, going into details about the main compo-nents, especially about the speech recognition engine. Then wedemonstrate our experimental results of the system and comparethem with laboratory test results.

In recent speech recognition systems the base unit of recognition is generally the speech sound. To each speech sound an acoustic model is associated, whose parameters are estimated by statistical methods. The proper training data fundamentally determine the efficiency of the recognizer. Present day technology and computational capacity allow speech recognition systems to operate with large dictionaries and complex language models, but the quality of the basic pattern matching units has large influence on the reliability of the system. In our experiments presented here we investigated the effects of different training methods to the recognition accuracy; namely, the effect of increasing the number of speakers and the number of mixtures were examined in the case of pronunciation modeling and context independent models.

In Hungarian, as more or less in many other languages, a large percent of words and phrases can be pronounced in several, different, but correct ways. Introducing pronunciation alternatives for individual vocabulary elements may improve the efficiency of the recognition. But in connected word recognition tasks the modeling of inter-word phonetic changes has a greater significance. In this paper we introduce a rule-based method for the automatic generation of pronunciation alternatives used first for isolated words and later the method is extended to handle cross-word phonological changes in recognition networks, applying a special approach applicable for the Hungarian language. To evaluate the method it is tested in connected number recognition tests.

In SpeechDat-Car, speech databases for speech driven devices and services for mobile environments are collected for nine European languages. The German SpeechDat-Car installation was the first fully equipped platform within the project. It has served as a testbed for the recording software for the entire project, and as an opportunity to perform technical and organizational feasibility tests for the German data collection. The main results are: i) drivers feel comfortable with the prompts displayed on an LCD screen attached to the dashboard, ii) the audio-visual mode of prompting allowed the elicitation of speech even under difficult traffic conditions, and iii) the original synchronization protocol could be made more efficient by 20%, so that the 129 items of one recording session can now be recorded in 45-50 minutes.