Evaluating the style of handwriting generation is a challenging problem, since it is not well defined. It is a key component in order to develop in developing systems with more personalized experiences with humans. In this paper, we propose baseline benchmarks, in order to set anchors to estimate the relative quality of different handwriting style methods. This will be done using deep learning techniques, which have shown remarkable results in different machine learning tasks, learning classification, regression, and most relevant to our work, generating temporal sequences. We discuss the challenges associated with evaluating our methods, which is related to evaluation of generative models in general. We then propose evaluation metrics, which we find relevant to this problem, and we discuss how we evaluate the evaluation metrics. In this study, we use IRON-OFF dataset. To the best of our knowledge, there is no work done before in generating handwriting (either in terms of methodology or the performance metrics), our in exploring styles using this dataset.
In order to simplify the procedure, and focus on our investigation of styles, we discretized the tracings using for direction, and speed - see Section more details -, and apply to the output of the last layer, instead of MDN. This was inspired by the results reported in @cite_2 @cite_11 , where they show impressive results on the discrete domain, given a good discretization policy. Having a categorical distribution is more flexible and generic that a continuous distribution, and requires no assumption about the data distribution shape. Recently, interesting work has been done concerning style extraction in the area of speech synthesis @cite_27 @cite_0 . In their work, they extract a number of . They evaluated the performance of their system via classical subjective rating of voice, and show these token relate to some aspects of the speech prosody and the speaker's voice.
@cite_0 @cite_27 @cite_11 @cite_2
2951423096 1575712594 2024536104 1987992317
A new type of End-to-End system for text-dependent speaker verification is presented in this paper. Previously, using the phonetically discriminative speaker discriminative DNNs as feature extractors for speaker verification has shown promising results. The extracted frame-level (DNN bottleneck, posterior or d-vector) features are equally weighted and aggregated to compute an utterance-level speaker representation (d-vector or i-vector). In this work we use speaker discriminative CNNs to extract the noise-robust frame-level features. These features are smartly combined to form an utterance-level speaker vector through an attention mechanism. The proposed attention model takes the speaker discriminative information and the phonetic information to learn the weights. The whole system, including the CNN and attention model, is joint optimized using an end-to-end criterion. The training algorithm imitates exactly the evaluation process --- directly mapping a test utterance and a few target speaker utterances into a single verification score. The algorithm can automatically select the most similar impostor for each target speaker to train the network. We demonstrated the effectiveness of the proposed end-to-end system on Windows @math "Hey Cortana" speaker verification task. Automatic recognition of a speaker’s emotions is a natural objective for research, but is difficult to gauge the level of performance that is currently attainable. We describe a study that offers a rough benchmark. Speech data came from five passages of about 100 syllables each. They had been selected following pilot studies because they were effective at evoking specific emotion fear, anger, happiness, sadness, and neutrality. 40 subjects were recorded reading them. A battery of 32 potentially relevant features was extracted using our ASSESS system. They were broadly speaking prosodic, derived from contours tracing the movement of intensity and pitch. They were input to statistical decision mechanisms, of two types. Discriminant analysis uses linear combinations of variables to separate samples that belong to different categories. There are reasons to suspect that linear combination will not be appropriate, so neural net classifiers were also considered. An automatic relevance determination procedure was used to identify the most relevant parameters. Discriminant analysis outperformed the neural networks. Using 90 of the data for training, and testing on the remaining 10 , a classification rate of 55 (+ 0.08 ) was achieved. The most useful predictors covered a variety of properties – intensity (relative to the start of the passage) and its spread; pitch spread; durations of silences, rises in intensity, and syllables; and a property related to the shape of ‘tunes’, the number of inflections in the F0 contour per tune. Many more variables were less important, but nevertheless contributed. We demonstrate a method for generating a 3D virtual character performance from the audio signal by inferring the acoustic and semantic properties of the utterance. Through a prosodic analysis of the acoustic signal, we perform an analysis for stress and pitch, relate it to the spoken words and identify the agitation state. Our rule-based system performs a shallow analysis of the utterance text to determine its semantic, pragmatic and rhetorical content. Based on these analyses, the system generates facial expressions and behaviors including head movements, eye saccades, gestures, blinks and gazes. Our technique is able to synthesize the performance and generate novel gesture animations based on coarticulation with other closely scheduled animations. Because our method utilizes semantics in addition to prosody, we are able to generate virtual character performances that are more appropriate than methods that use only prosody. We perform a study that shows that our technique outperforms methods that use prosody alone. In this paper, we propose a postfilter to compensate modulation spectrum in HMM-based speech synthesis. In order to alleviate over-smoothing effects which is a main cause of quality degradation in HMM-based speech synthesis, it is necessary to consider features that can capture over-smoothing. Global Variance (GV) is one well-known example of such a feature, and the effectiveness of parameter generation algorithm considering GV have been confirmed. However, the quality gap between natural speech and synthetic speech is still large. In this paper, we introduce the Modulation Spectrum (MS) of speech parameter trajectory as a new feature to effectively capture the over-smoothing effect, and we propose a postfilter based on the MS. The MS is represented as a power spectrum of the parameter trajectory. The generated speech parameter sequence is filtered to ensure that its MS has a pattern similar to natural speech. Experimental results show quality improvements when the proposed methods are applied to spectral and F 0 components, compared with conventional methods considering GV.