@inproceedings{HoenigBockletRiedhammeretal.2012, author = {H{\"o}nig, Florian and Bocklet, Tobias and Riedhammer, Korbinian and Batliner, Anton and N{\"o}th, Elmar}, title = {The Automatic Assessment of Non-native Prosody: Combining Classical Prosodic Analysis with Acoustic Modelling}, series = {INTERSPEECH 2012, 13th Annual Conference of the International Speech Communication Association (ISCA), Portland, OR, USA, September 2012.}, booktitle = {INTERSPEECH 2012, 13th Annual Conference of the International Speech Communication Association (ISCA), Portland, OR, USA, September 2012.}, year = {2012}, abstract = {In earlier studies, we assessed the degree of non-nativeness employing prosodic information. In this paper, we combine prosodic information with (1) features derived from a Gaussian Mixture Model used as Universal Background Model (GMM-UBM), a powerful approach used in speaker identification, and (2) openSMILE, a standard open-source toolkit for extracting acoustic features. We evaluate our approach with English speech from 94 non-native speakers. GMM-UBM or openSMILE modelling alone yields lower performance than our prosodic feature vector; however, adding information from the GMM-UBM modelling or openSMILE by late fusion improves results.}, language = {en} } @inproceedings{RiedhammerBockletGoshaletal.2012, author = {Riedhammer, Korbinian and Bocklet, Tobias and Goshal, Arnab and Povey, Daniel}, title = {Revisiting Semi-Continuous Hidden Markov Models}, series = {2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan, March 2012.}, booktitle = {2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan, March 2012.}, publisher = {IEEE}, pages = {4721 -- 4724}, year = {2012}, abstract = {In the past decade, semi-continuous hidden Markov models (SCHMMs) have not attracted much attention in the speech recognition community. Growing amounts of training data and increasing sophistication of model estimation led to the impression that continuous HMMs are the best choice of acoustic model. However, recent work on recognition of under-resourced languages faces the same old problem of estimating a large number of parameters from limited amounts of transcribed speech. This has led to a renewed interest in methods of reducing the number of parameters while maintaining or extending the modeling capabilities of continuous models. In this work, we compare classic and multiple-codebook semi-continuous models using diagonal and full covariance matrices with continuous HMMs and subspace Gaussian mixture models. Experiments on the RM and WSJ corpora show that while a classical semicontinuous system does not perform as well as a continuous one, multiple-codebook semi-continuous systems can perform better, particular when using full-covariance Gaussians.}, language = {en} } @article{BockletRiedhammerNoethetal.2012, author = {Bocklet, Tobias and Riedhammer, Korbinian and N{\"o}th, Elmar and Eysholdt, Ulrich and Haderlein, Tino}, title = {Automatic Intelligibility Assessment of Speakers After Laryngeal Cancer by Means of Acoustic Modeling}, series = {Journal of Voice}, journal = {Journal of Voice}, number = {26(3)}, pages = {390 -- 397}, year = {2012}, abstract = {One aspect of voice and speech evaluation after laryngeal cancer is acoustic analysis. Perceptual evaluation by expert raters is a standard in the clinical environment for global criteria such as overall quality or intelligibility. So far, automatic approaches evaluate acoustic properties of pathologic voices based on voiced/unvoiced distinction and fundamental frequency analysis of sustained vowels. Because of the high amount of noisy components and the increasing aperiodicity of highly pathologic voices, a fully automatic analysis of fundamental frequency is difficult. We introduce a purely data-driven system for the acoustic analysis of pathologic voices based on recordings of a standard text.}, language = {en} }