Browsing by Issue Date, starting with "2017-08-20"
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- Automatic Evaluation of Children Reading Aloud on Sentences and PseudowordsPublication . Proença, Jorge; Lopes, Carla; Tjalve, Michael; Stolcke, Andreas; Candeias, Sara; Perdigão, FernandoReading aloud performance in children is typically assessed by teachers on an individual basis, manually marking reading time and incorrectly read words. A computational tool that assists with recording reading tasks, automatically analyzing them and providing performance metrics could be a significant help. Towards that goal, this work presents an approach to automatically predicting the overall reading aloud ability of primary school children (6-10 years old), based on the reading of sentences and pseudowords. The opinions of primary school teachers were gathered as ground truth of performance, who provided 0-5 scores closely related to the expectations at the end of each grade. To predict these scores automatically, features based on reading speed and number of disfluencies were extracted, after an automatic disfluency detection. Various regression models were trained, with Gaussian process regression giving best results for automatic features. Feature selection from both sentence and pseudoword reading tasks gave the closest predictions, with a correlation of 0.944. Compared to the use of manual annotation with the best correlation being 0.952, automatic annotation was only 0.8% worse. Furthermore, the error rate of predicted scores relative to ground truth was found to be smaller than the deviation of evaluators’ opinion per child.
- Detection of Mispronunciations and Disfluencies in Children Reading AloudPublication . Proença, Jorge; Lopes, Carla; Tjalve, Michael; Stolcke, Andreas; Candeias, Sara; Perdigão, FernandoTo automatically evaluate the performance of children reading aloud or to follow a child’s reading in reading tutor applications, different types of reading disfluencies and mispronunciations must be accounted for. In this work, we aim to detect most of these disfluencies in sentence and pseudoword reading. Detecting incorrectly pronounced words, and quantifying the quality of word pronunciations, is arguably the hardest task. We approach the challenge as a two-step process. First, a segmentation using task-specific lattices is performed, while detecting repetitions and false starts and providing candidate segments for words. Then, candidates are classified as mispronounced or not, using multiple features derived from likelihood ratios based on phone decoding and forced alignment, as well as additional meta-information about the word. Several classifiers were explored (linear fit, neural networks, support vector machines) and trained after a feature selection stage to avoid overfitting. Improved results are obtained using feature combination compared to using only the log likelihood ratio of the reference word (22% versus 27% miss rate at constant 5% false alarm rate).
- Segment Level Voice Conversion with Recurrent Neural NetworksPublication . Ramos, Miguel Varela; Black, Alan W.; Astudillo, Ramon Fernandez; Trancoso, Isabel; Fonseca, NunoVoice conversion techniques aim to modify a subject’s voice characteristics in order to mimic the one’s of another person. Due to the difference in utterance length between source and target speaker, state of the art voice conversion systems often rely on a frame alignment pre-processing step. This step aligns the entire utterances with algorithms such as dynamic time warping (DTW) that introduce errors, hindering system performance. In this paper we present a new technique that avoids the alignment of entire utterances at frame level, while keeping the local context during training. For this purpose, we combine an RNN model with the use of phoneme or syllablelevel information, obtained from a speech recognition system. This system segments the utterances into segments which then can be grouped into overlapping windows, providing the needed context for the model to learn the temporal dependencies. We show that with this approach, notable improvements can be attained over a state of the art RNN voice conversion system on the CMU ARCTIC database. It is also worth noting that with this technique it is possible to halve the training data size and still outperform the baseline.