arXiv:2608.22872v1 Announce Type: new Abstract: Speech-based applications pass spoken queries through automatic speech recognition (ASR) before any retrieval module, so ASR errors enter the pipeline...
arXiv:2602. 12783v3 Announce Type: replace-cross Abstract: Spoken query retrieval is an important interaction mode in modern information retrieval.
arXiv:2412. 15529v4 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) synergizes the retrieval of pertinent data with the generative capabilities of Large Language Models (LLMs), ensuring that the generated output is not only contextually relevant but also accurate and current.
arXiv:2608.28916v1 Announce Type: new Abstract: Automatic speech recognition (ASR) systems are commonly evaluated with word error rate (WER), yet many voice workflows depend on exact written values f...
The paper introduces GUIDE, a generative unsupervised framework for Chinese query correction that uses shared-ID encoding for phonetically or visually confusable characters and an encoder–decoder architecture to reconstruct queries within plausible confusion neighborhoods. It incorporates a time‑decayed, query‑frequency‑weighted objective to adapt to rapidly changing query vocabularies. Experiments on QSpell 250K and the large‑scale KwaiSearch dataset demonstrate that GUIDE consistently outperforms strong baselines, with online A/B testing confirming improvements in correction quality and downstream engagement.
The paper introduces a unified phoneme‑based TTS‑to‑ASR augmentation pipeline that uses a multilingual TTS model with language‑ID conditioning and incorporates grapheme‑to‑phoneme conversion, reference‑speech filtering, and candidate‑text selection. It proposes phoneme‑frequency‑guided selection (PFGS) to rank sentences based on phoneme frequencies from real ASR labels, and demonstrates that random augmentation and PFGS both improve ASR performance across Arabic, French, Italian, and Portuguese test sets, with PFGS yielding up to a 19.3% relative WER reduction. The study also shows that filtering reference speech can further lower WER by up to 0.59 points on certain datasets.
The paper introduces a compact BART-based sequence‑to‑sequence model for low‑latency spell correction of Japanese music search queries, addressing challenges posed by four writing scripts. It employs a script‑aware synthetic misspelling pipeline that blends keyboard‑layout models, phonetic confusion priors, consonant alternations, and kana case errors, and normalizes mixed‑script titles to a single canonical script to reduce hallucinations. Experiments show the model achieves 41.09% exact‑match accuracy and 11.62% CER, outperforming edit‑distance baselines while keeping inference latency under 4 ms on a single GPU.
The paper introduces DEX-Comp, a two‑stage training method for soft context compression in Retrieval‑Augmented Generation (RAG). First, a pure distillation warm‑start trains the compression model on correct responses from an uncompressed RAG. Then, hard exploration uses reinforcement learning on queries where the uncompressed RAG fails, encouraging better computation patterns for compressed representations. Experiments on five open‑domain QA benchmarks show that DEX‑Comp compresses retrieved contexts 16×, speeds inference 4×–24×, and matches or surpasses the uncompressed RAG baseline across various retrieval depths.
arXiv:2608.15851v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) systems rely on retrieval modules to ground large language model (LLM) outputs. LLM-based query expansio...
arXiv:2608. 08569v1 Announce Type: new Abstract: Recent advancements in Speech Large Language Models have demonstrated remarkable capabilities in understanding complex audio tasks.
Recent advances in zero-shot text-to-speech (TTS) have substantially improved speech quality and voice cloning fidelity. However, many zero-shot TTS systems still depend on audio prompt transcripts at inference time.
arXiv:2606. 20518v1 Announce Type: new Abstract: Flow-matching text-to-speech systems achieve remarkable zero-shot quality but remain static after deployment: pronunciation errors on out-of-vocabulary proper nouns persist unless the model is retrained.