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Found 12 papers, 5 papers with code
CONCSS: Contrastive-based Context Comprehension for Dialogue-appropriate Prosody in Conversational Speech Synthesis
In this paper, we introduce a contrastive learning-based CSS framework, CONCSS.
Rhythm-controllable Attention with High Robustness for Long Sentence Speech Synthesis
Regressive Text-to-Speech (TTS) system utilizes attention mechanism to generate alignment between text and acoustic feature sequence.
Text-Aware End-to-end Mispronunciation Detection and Diagnosis
In the field of assessing the pronunciation quality of constrained speech, the given transcriptions can play the role of a teacher.
An Empirical Study on End-to-End Singing Voice Synthesis with Encoder-Decoder Architectures
With the rapid development of neural network architectures and speech processing models, singing voice synthesis with neural networks is becoming the cutting-edge technique of digital music production.
Speech Enhancement using Separable Polling Attention and Global Layer Normalization followed with PReLU
With all these modifications, the size of the PHASEN model is shrunk from 33M parameters to 5M parameters, while the performance on VoiceBank+DEMAND is improved to the CSIG score of 4. 30, the PESQ score of 3. 07 and the COVL score of 3. 73.
A Full Text-Dependent End to End Mispronunciation Detection and Diagnosis with Easy Data Augmentation Techniques
Recently, end-to-end mispronunciation detection and diagnosis (MD&D) systems has become a popular alternative to greatly simplify the model-building process of conventional hybrid DNN-HMM systems by representing complicated modules with a single deep network architecture.
Dynamically Mitigating Data Discrepancy with Balanced Focal Loss for Replay Attack Detection
Besides, in the experiments, we select three kinds of features that contain both magnitude-based and phase-based information to form complementary and informative features.
Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment Analysis
Therefore, in this paper, based on audio and text, we consider the task of multimodal sentiment analysis and propose a novel fusion strategy including both multi-feature fusion and multi-modality fusion to improve the accuracy of audio-text sentiment analysis.
Multimodal Emotion Recognition
Multimodal Sentiment Analysis
Boosting Noise Robustness of Acoustic Model via Deep Adversarial Training
In realistic environments, speech is usually interfered by various noise and reverberation, which dramatically degrades the performance of automatic speech recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Trainable back-propagated functional transfer matrices
In experiments, it is demonstrated that the revised rules can be used to train a range of functional connections: 20 different functions are applied to neural networks with up to 10 hidden layers, and most of them gain high test accuracies on the MNIST database.
Learning of Human-like Algebraic Reasoning Using Deep Feedforward Neural Networks
Briefly, in a reasoning system, a deep feedforward neural network is used to guide rewriting processes after learning from algebraic reasoning examples produced by humans.
