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Found 11 papers, 1 papers with code
Creating an African American-Sounding TTS: Guidelines, Technical Challenges,and Surprising Evaluations
Representations of AI agents in user interfaces and robotics are predominantly White, not only in terms of facial and skin features, but also in the synthetic voices they use.
Speak While You Think: Streaming Speech Synthesis During Text Generation
Experimental results show that LLM2Speech maintains the teacher's quality while reducing the latency to enable natural conversations.
Towards a Common Speech Analysis Engine
Beyond that, a common engine should be capable of supporting distributed training with client in-house private data.
A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets
The NNSI reduces the need for manual labeling by automatically selecting highly-ambiguous samples and labeling them with high accuracy.
Speech Emotion Recognition using Self-Supervised Features
Self-supervised pre-trained features have consistently delivered state-of-art results in the field of natural language processing (NLP); however, their merits in the field of speech emotion recognition (SER) still need further investigation.
Speaker Normalization for Self-supervised Speech Emotion Recognition
Large speech emotion recognition datasets are hard to obtain, and small datasets may contain biases.
Ranked #1 on
Speech Emotion Recognition
on IEMOCAP
(AUC metric)
RNN Transducer Models For Spoken Language Understanding
We present a comprehensive study on building and adapting RNN transducer (RNN-T) models for spoken language understanding(SLU).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systems
Assuming we have additional text-to-intent data (without speech) available, we investigated two techniques to improve the S2I system: (1) transfer learning, in which acoustic embeddings for intent classification are tied to fine-tuned BERT text embeddings; and (2) data augmentation, in which the text-to-intent data is converted into speech-to-intent data using a multi-speaker text-to-speech system.
End-to-End Spoken Language Understanding Without Full Transcripts
For our speech-to-entities experiments on the ATIS corpus, both the CTC and attention models showed impressive ability to skip non-entity words: there was little degradation when trained on just entities versus full transcripts.
Siamese x-vector reconstruction for domain adapted speaker recognition
With the rise of voice-activated applications, the need for speaker recognition is rapidly increasing.
High quality, lightweight and adaptable TTS using LPCNet
We first demonstrate the ability of the system to produce high quality speech when trained on large, high quality datasets.
Audio and Speech Processing Sound
