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Found 6 papers, 3 papers with code
Enhancing the Stability of LLM-based Speech Generation Systems through Self-Supervised Representations
Using speaker-disentangled codes to train LLMs for text-to-speech (TTS) allows the LLM to generate the content and the style of the speech only from the text, similarly to humans, while the speaker identity is provided by the decoder of the VC model.
Empirical study of the modulus as activation function in computer vision applications
In this work we propose a new non-monotonic activation function: the modulus.
Stutter-TTS: Controlled Synthesis and Improved Recognition of Stuttered Speech
By fine-tuning an ASR model on synthetic stuttered speech we are able to reduce word error by 5. 7% relative on stuttered utterances, with only minor (<0. 2% relative) degradation for fluent utterances.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Approaching sales forecasting using recurrent neural networks and transformers
Accurate and fast demand forecast is one of the hot topics in supply chain for enabling the precise execution of the corresponding downstream processes (inbound and outbound planning, inventory placement, network planning, etc).
End-to-end Keyword Spotting using Xception-1d
The field of conversational agents is growing fast and there is an increasing need for algorithms that enhance natural interaction.
Improving multi-speaker TTS prosody variance with a residual encoder and normalizing flows
This paper proposes a new neural text-to-speech model that approaches the disentanglement problem by conditioning a Tacotron2-like architecture on flow-normalized speaker embeddings, and by substituting the reference encoder with a new learned latent distribution responsible for modeling the intra-sentence variability due to the prosody.
