Continuous Speech Synthesis using per-token Latent Diffusion

The success of autoregressive transformer models with discrete tokens has inspired quantization-based approaches for continuous modalities, though these often limit reconstruction quality. We therefore introduce SALAD, a per-token latent diffusion model for zero-shot text-to-speech, that operates on continuous representations. SALAD builds upon the recently proposed expressive diffusion head for image generation, and extends it to generate variable-length outputs. Our approach utilizes semantic tokens for providing contextual information and determining the stopping condition. We suggest three continuous variants for our method, extending popular discrete speech synthesis techniques. Additionally, we implement discrete baselines for each variant and conduct a comparative analysis of discrete versus continuous speech modeling techniques. Our results demonstrate that both continuous and discrete approaches are highly competent, and that SALAD achieves a superior intelligibility score while obtaining speech quality and speaker similarity on par with the ground-truth audio.

Low Bitrate High-Quality RVQGAN-based Discrete Speech Tokenizer

Discrete Audio codecs (or audio tokenizers) have recently regained interest due to the ability of Large Language Models (LLMs) to learn their compressed acoustic representations. Various publicly available trainable discrete tokenizers recently demonstrated impressive results for audio tokenization, yet they mostly require high token rates to gain high-quality reconstruction. In this study, we fine-tuned an open-source general audio RVQGAN model using diverse open-source speech data, considering various recording conditions and quality levels. The resulting wideband (24kHz) speech-only model achieves speech reconstruction, which is nearly indistinguishable from PCM (pulse-code modulation) with a rate of 150-300 tokens per second (1500-3000 bps). The evaluation used comprehensive English speech data encompassing different recording conditions, including studio settings. Speech samples are made publicly available in http://ibm.biz/IS24SpeechRVQ . The model is officially released in https://huggingface.co/ibm/DAC.speech.v1.0

Exploring the Benefits of Tokenization of Discrete Acoustic Units

Tokenization algorithms that merge the units of a base vocabulary into larger, variable-rate units have become standard in natural language processing tasks. This idea, however, has been mostly overlooked when the vocabulary consists of phonemes or Discrete Acoustic Units (DAUs), an audio-based representation that is playing an increasingly important role due to the success of discrete language-modeling techniques. In this paper, we showcase the advantages of tokenization of phonetic units and of DAUs on three prediction tasks: grapheme-to-phoneme, grapheme-to-DAUs, and unsupervised speech generation using DAU language modeling. We demonstrate that tokenization yields significant improvements in terms of performance, as well as training and inference speed, across all three tasks. We also offer theoretical insights to provide some explanation for the superior performance observed.

Speak While You Think: Streaming Speech Synthesis During Text Generation

Large Language Models (LLMs) demonstrate impressive capabilities, yet interaction with these models is mostly facilitated through text. Using Text-To-Speech to synthesize LLM outputs typically results in notable latency, which is impractical for fluent voice conversations. We propose LLM2Speech, an architecture to synthesize speech while text is being generated by an LLM which yields significant latency reduction. LLM2Speech mimics the predictions of a non-streaming teacher model while limiting the exposure to future context in order to enable streaming. It exploits the hidden embeddings of the LLM, a by-product of the text generation that contains informative semantic context. Experimental results show that LLM2Speech maintains the teacher's quality while reducing the latency to enable natural conversations.

Active Learning Through a Covering Lens

Deep active learning aims to reduce the annotation cost for deep neural networks, which are notoriously data-hungry. Until recently, deep active learning methods struggled in the low-budget regime, where only a small amount of samples are annotated. The situation has been alleviated by recent advances in self-supervised representation learning methods, which impart the geometry of the data representation with rich information about the points. Taking advantage of this progress, we study the problem of subset selection for annotation through a "covering" lens, proposing ProbCover -- a new active learning algorithm for the low budget regime, which seeks to maximize Probability Coverage. We describe a dual way to view our formulation, from which one can derive strategies suitable for the high budget regime of active learning, related to existing methods like Coreset. We conclude with extensive experiments, evaluating ProbCover in the low budget regime. We show that our principled active learning strategy improves the state-of-the-art in the low-budget regime in several image recognition benchmarks. This method is especially beneficial in semi-supervised settings, allowing state-of-the-art semi-supervised methods to achieve high accuracy with only a few labels.

Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets

Investigating active learning, we focus on the relation between the number of labeled examples (budget size), and suitable corresponding querying strategies. Our theoretical analysis shows a behavior reminiscent of phase transition: typical points should best be queried in the low budget regime, while atypical (or uncertain) points are best queried when the budget is large. Combined evidence from our theoretical and empirical studies shows that a similar phenomenon occurs in simple classification models. Accordingly, we propose TypiClust -- a deep active learning strategy suited for low budgets. In a comparative empirical investigation using a variety of architectures and image datasets, we report that in the low budget regime, TypiClust outperforms all other active learning strategies. Using TypiClust in a semi-supervised framework, the performance of competitive semi-supervised methods gets a significant boost, surpassing the state of the art.