Organizations: MoE Key Lab of Artificial Intelligence, AI Institute, X-LANCE Lab, Shanghai Jiao Tong University · School of Intelligence Science and Technology, Nanjing University, Suzhou, China
Prompt-based contextual ASR requires selecting a compact hotword subset from large dynamic inventories, yet existing pre-selection methods rely either on discrete ASR hypotheses that may discard acoustic evidence or on separately trained retrieval models. We propose Viterbi-guided Selection of Hotwords (VISH), a training-free, plug-and-play framework that directly verifies candidate-specific acoustic support over frame-level CTC posteriors. VISH formulates pre-selection as constrained posterior search; M-VISH makes large-list verification scalable through frame-synchronous shared-prefix Viterbi search with bounded top-K pruning, while Bi-MVISH exploits complementary backward evidence for robust verification. Across five benchmarks, M-VISH achieves up to 11.7% absolute improvement in Best-F1 over a strong learned-retrieval baseline and reduces candidate-search latency by 75.2% at 2,000 candidates relative to hypothesis-based filtering. These results demonstrate the effectiveness of posterior-level verification for accurate and scalable hotword pre-selection without additional model training.
Figures & tables
Figure 1: Overview of VISH and the M-VISH search procedure. (a) VISH verifies candidate hotwords using internal or external CTC posteriors and selects a compact subset for contextual prompting without modifying the downstream ASR model. (b) A shared-prefix trie organizes candidate CTC-token sequences to reuse Viterbi computations across candidates. (c) At each retained frame, M-VISH updates constrained Viterbi scores, retains the top- Kp active prefixes, and records scores at terminal nodes for scalable large-list verification. Here, pt(k) denotes the CTC posterior of token k at frame t , and ℓ denotes the trie depth.
Method
STOP1
STOP2
A1-NE
L-clean-2k
L-other-2k
GLCLAP [ 15 ]
–
–
97.0
–
–
CLAR [ 11 ]
–
–
97.0
–
–
GLCLAP [ 15 ] ∗
80.5
81.2
96.6
90.5
82.4
M-VISH
83.1
92.9
97.5
94.4
87.7
Bi-MVISH
83.1
93.8
97.6
95.3
88.8
Table 1: Hotword pre-selection performance. Best-F1 (%) across five benchmarks. A1-NE denotes AISHELL-1-NE; L-clean-2k and L-other-2k denote LibriSpeech test-clean and test-other with 2,000 candidate hotwords, respectively. ∗ denotes our reproduced results; “–” indicates unreported results. Best results are bold.
ASR System
L-clean-2k
L-other-2k
A1-NE
Whisper-medium [ 22 ]
+No Bias
3.1/14.0
7.1/29.9
7.3/21.3
+GT Bias
2.2/6.2
5.6/15.9
4.3/7.4
+CTC-Assistant [ 30 ] ∗
2.4/7.8
6.2/18.8
4.7/9.9
+GLCLAP [ 15 ] ∗
2.4/7.6
6.1/18.2
4.6/9.1
+M-VISH
2.4/7.5
6.0/18.3
4.4 /7.8
Table 2: Downstream contextual ASR performance. WER/B-WER (%) are reported for LibriSpeech and CER/B-CER (%) for AISHELL-1-NE. GT Bias denotes oracle hotword prompting. ∗ denotes our reproduced results. N/A indicates Prompt-SLAM-ASR model does not support Mandarin. Best non-oracle results are bold.
Encoder
Params.
Extra
Test set
Best-F1
WER/B-WER
SenseVoice-Small (external)
234M
+234M
Clean
95.3
1.4 / 3.7
Other
88.8
3.1/ 8.0
WavLM-Large (internal)
315M
0
Clean
94.7
1.4 /3.9
Other
87.8
3.0 /8.6
Table 3: Effect of CTC posterior source. Comparison of internal and external CTC encoders for Bi-MVISH with Prompt-SLAM-ASR using 2,000 candidate hotwords per utterance. Extra denotes additional encoder parameters beyond the ASR backbone. Clean and Other refer to LibriSpeech test-clean and test-other, respectively. Best results are bold.
Bias List Size
100
500
1,000
2,000
CTC-Assistant [ 30 ]
46
206
398
817
GLCLAP [ 15 ]
61
65
74
78
M-VISH
71
85
117
203
Bi-MVISH
131
176
260
400
Table 4: Candidate-search efficiency. Average search latency (ms per utterance) with different bias list sizes, measured as the number of candidate hotwords per utterance. Acoustic representation extraction is excluded.
Figure 2: Forward and backward Bi-MVISH alignments for “liddy” (left) and “smolny” (right). Both directions detect “liddy”, whereas backward search recovers stronger evidence for “smolny” with its weak onset. Yellow dotted lines denote word boundaries.
Institute of Information Engineering, Chinese Academy of Sciences · School of Cyber Security, University of Chinese Academy of Sciences · MiLM Plus, Xiaomi Inc.