13 citations · 20 across the 8 of their papers we have counts for
8 papers
MagpieTTS-LF: Inference-Time Long-Form Speech Generation Without Training on Long-Form data
Subhankar Ghosh, Jason Li, Paarth Neekhara +4
Neural Text-to-Speech (TTS) systems achieve remarkable quality on short utterances but long-form speech generation shows prosodic drift, speaker inconsistencies and sentence bounda…
Align2Speak: Improving TTS for Low Resource Languages via ASR-Guided Online Preference Optimization
Shehzeen Hussain, Paarth Neekhara, Xuesong Yang +7
Developing high-quality text-to-speech (TTS) systems for low-resource languages is challenging due to the scarcity of paired text and speech data. In contrast, automatic speech rec…
NanoCodec: Towards High-Quality Ultra Fast Speech LLM Inference
Edresson Casanova, Paarth Neekhara, Ryan Langman +6
Large Language Models (LLMs) have significantly advanced audio processing by leveraging audio codecs to discretize audio into tokens, enabling the application of language modeling…
HiFiTTS-2: A Large-Scale High Bandwidth Speech Dataset
Ryan Langman, Xuesong Yang, Paarth Neekhara +4
This paper introduces HiFiTTS-2, a large-scale speech dataset designed for high-bandwidth speech synthesis. The dataset is derived from LibriVox audiobooks, and contains approximat…
Low Frame-rate Speech Codec: a Codec Designed for Fast High-quality Speech LLM Training and Inference
Edresson Casanova, Ryan Langman, Paarth Neekhara +5
Large language models (LLMs) have significantly advanced audio processing through audio codecs that convert audio into discrete tokens, enabling the application of language modelin…
Codec-ASR: Training Performant Automatic Speech Recognition Systems with Discrete Speech Representations
Kunal Dhawan, Nithin Rao Koluguri, Ante Jukić +3
Discrete speech representations have garnered recent attention for their efficacy in training transformer-based models for various speech-related tasks such as automatic speech rec…
Spectral Codecs: Improving Non-Autoregressive Speech Synthesis with Spectrogram-Based Audio Codecs
Ryan Langman, Ante Jukić, Kunal Dhawan +2
Historically, most speech models in machine-learning have used the mel-spectrogram as a speech representation. Recently, discrete audio tokens produced by neural audio codecs have…
Improving fairness in speaker verification via Group-adapted Fusion Network
Hua Shen, Yuguang Yang, Guoli Sun +4
Modern speaker verification models use deep neural networks to encode utterance audio into discriminative embedding vectors. During the training process, these networks are typical…