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arxiv: 2604.12398 · v1 · submitted 2026-04-14 · 📡 eess.AS

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Contextual Biasing for ASR in Speech LLM with Common Word Cues and Bias Word Position Prediction

Sashi Novitasari , Takashi Fukuda , Kurata Gakuto , George Saon
Authors on Pith no claims yet

Pith reviewed 2026-05-10 14:32 UTC · model grok-4.3

classification 📡 eess.AS
keywords contextual biasingspeech LLMASRbias wordsacoustic cuesposition predictionout-of-domain
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The pith

Speech LLMs use acoustic cues from common words to recognize rare bias words without phoneme tools.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Speech-aware LLMs still miss words that appear rarely in training even when given a list of expected terms. The paper replaces explicit phoneme sequences or G2P converters with acoustic patterns drawn from common words whose pronunciations overlap only partially with the target bias words. It adds a separate head that predicts the positions of those bias words inside the output, trained jointly with the main recognition task. The combined method lowers bias-word errors by 16.3 percent relative to standard baselines and keeps the gain on out-of-domain data. No phonetic expertise or language-specific pronunciation software is required at inference time.

Core claim

By pairing each bias word with acoustic cues taken from common words that sound partially similar and by training the model to predict bias-word locations in a multi-output setup, the speech LLM transcribes uncommon terms more reliably without any G2P system or manual phoneme input.

What carries the argument

Acoustic cues from common words with partially matching pronunciations, combined with bias-word position prediction trained as multi-output learning.

Load-bearing premise

Partial pronunciation similarity between common words and bias words supplies acoustic cues strong enough to steer the model toward the correct rare word.

What would settle it

A test set containing only bias words that share no phonetic overlap with any common words in the training vocabulary, measured for whether the 16.3 percent error reduction vanishes.

read the original abstract

Speech-aware LLMs (SLLMs) have recently achieved state-of-the-art ASR performance; however, they still fail to accurately transcribe bias words that appear rarely or never in the training data. Contextual biasing mechanisms are commonly implemented by introducing a predefined bias word list into the model via a text prompt or additional module. For further improvement, predefined bias words can be paired with their phoneme representations as pronunciation cues. Typically, phoneme sequences are generated through a G2P system that covers the target languages and domains of the bias words. Therefore, when a compatible G2P system is unavailable, phoneme-assisted contextual biasing becomes difficult to perform. Moreover, manually adding accurate phoneme sequences requires advanced phonetic knowledge. In this paper, we explore contextual biasing in SLLM based on acoustic cues associated with a set of common words whose pronunciations are partially similar to those of the target bias words. We assume ASR applications in which end users do not require special knowledge of phonetics or utilize G2P tools for inference. For enhanced robustness, we also introduce bias word positional prediction implemented in a multi-output learning fashion. Our method reduces bias word recognition errors by 16.3% compared to baseline systems, including on out-of-domain data.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper proposes contextual biasing for speech-aware LLMs (SLLMs) in ASR by pairing target bias words with acoustic cues from common words whose pronunciations are partially similar, avoiding G2P systems and phonetic expertise. It adds bias word position prediction via multi-output learning for robustness. The central claim is a 16.3% relative reduction in bias word recognition errors versus baselines, holding on out-of-domain data.

Significance. If the performance gains are substantiated with full experimental details, the work could meaningfully advance accessible contextual biasing in SLLMs by removing dependencies on G2P tools or expert phonetics, particularly for rare or OOD bias terms. The empirical focus on real-world usability is a strength, though the absence of ablations, dataset specifications, or statistical validation limits immediate impact assessment.

major comments (2)
  1. [Abstract] Abstract: The 16.3% relative error reduction on bias words (including OOD) is presented without any baseline system descriptions, dataset sizes/domains, bias word counts, statistical significance tests, or ablation results. This leaves the central performance claim weakly supported and difficult to reproduce or compare.
  2. [Method] Method description (inferred from abstract and approach): The core mechanism for automatically identifying common words with partially similar pronunciations and extracting their acoustic cues for input to the SLLM is not specified. It remains unclear whether this pairing relies on precomputed similarities, external tools, or manual steps, which directly undermines the claim of no G2P or phonetic expertise required.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful review and constructive feedback. We address each major comment below with point-by-point responses. Where the comments highlight opportunities for improved clarity or additional support, we have incorporated revisions; where details already exist in the manuscript, we clarify their location and strengthen presentation as needed.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The 16.3% relative error reduction on bias words (including OOD) is presented without any baseline system descriptions, dataset sizes/domains, bias word counts, statistical significance tests, or ablation results. This leaves the central performance claim weakly supported and difficult to reproduce or compare.

    Authors: We agree that the abstract, due to length constraints, omits experimental specifics that would better contextualize the 16.3% figure. The full manuscript details the baseline systems (including standard SLLM prompting and phoneme-based biasing where applicable) in Section 3, dataset sizes/domains and OOD splits in Section 4, bias word counts and selection in Section 4.1, and ablation studies isolating the common-word cue and position-prediction components in Section 5. The 16.3% relative reduction is computed across these controlled comparisons. We did not report formal statistical tests in the original submission. In revision we will add bootstrap confidence intervals or paired significance tests to support the claim and will expand the abstract with one sentence summarizing dataset scale and primary baselines. revision: partial

  2. Referee: [Method] Method description (inferred from abstract and approach): The core mechanism for automatically identifying common words with partially similar pronunciations and extracting their acoustic cues for input to the SLLM is not specified. It remains unclear whether this pairing relies on precomputed similarities, external tools, or manual steps, which directly undermines the claim of no G2P or phonetic expertise required.

    Authors: The referee correctly notes that the abstract alone does not fully specify the identification step. The approach section of the manuscript describes an automatic pipeline that selects common words via embedding similarity (computed from the SLLM encoder outputs on common-word audio segments) to the target bias words, then feeds the corresponding encoder acoustic representations directly as cues. No G2P conversion, external phonetic tools, or manual phoneme annotation is used at any stage; the process operates entirely on existing audio-text pairs and model embeddings. To eliminate any ambiguity, we will add a dedicated paragraph with pseudocode and a diagram in the revised method section that explicitly walks through the selection and cue-extraction steps, thereby reinforcing the claim that the method requires neither G2P systems nor phonetic expertise. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical method with measured results

full rationale

The paper describes an empirical technique for contextual biasing in speech-aware LLMs that pairs acoustic cues from common words with bias lists and adds multi-output bias-word position prediction. It reports concrete error-rate reductions (16.3 % relative) on in-domain and out-of-domain test sets against baseline systems. No equations, derivations, fitted parameters presented as predictions, or load-bearing self-citations appear in the provided text. The central claims rest on experimental measurements rather than any chain that reduces outputs to inputs by construction, satisfying the default expectation of a non-circular empirical study.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on the domain assumption that partial pronunciation overlap between common words and bias words supplies usable acoustic cues; no free parameters, axioms, or invented entities are explicitly introduced in the abstract.

pith-pipeline@v0.9.0 · 5536 in / 1007 out tokens · 30453 ms · 2026-05-10T14:32:19.818080+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

59 extracted references · 8 canonical work pages · 4 internal anchors

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    Contextual Biasing for ASR in Speech LLM with Common Word Cues and Bias Word Position Prediction

    INTRODUCTION Automatic speech recognition (ASR) technology has advanced rapidly in recent decades. The latest developments have enabled ASR in large language models (LLMs) to achievestate-of-the-art performance. In particular, speech-aware text LLM (SLLM) frame- works [1, 2, 3, 4] have gained attention for their remarkable mod- ularity and performance. Th...

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    We demonstrate word-level cue selections based on phonetic (pronunciation) and structural (spelling) similarities between the common word and the bias word

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    METHODOLOGY 2.1. Contextual biasing via textual prompt Our model employs a textual prompt-based approach to perform contextual biasing on ASR tasks. It takes input consisting of a speech audioS= [s 1, s2, ..., sI ]withIframes, a textual task instructionX= [x 1, x2, ..., xJ ]withJwords (e.g., ”Transcribe this speech”), and a bias word listB= [b 1, b2, ...,...

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    EXPERIMENT SETTING 3.1. Model We used the Granite-Speech [4] 1 architecture as our SLLM back- bone, which was originally designed for ASR and speech translation tasks. In our experiments, we focus on English ASR tasks. It con- sists of a speech encoder, a projector, and a text LLM. The speech encoder has a Conformer-CTC structure with 10 Conformer blocks,...

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