Blending LLMs into Cascaded Speech Translation: KIT’s Offline Speech Translation System for IWSLT 2024 (2024)

Abstract: Large Language Models (LLMs) are currently under exploration for various tasks, including Automatic Speech Recognition (ASR), Machine Translation (MT), and even End-to-End Speech Translation (ST). In this paper, we present KIT’s offline submission in the constrained + LLM track by incorporating recently proposed techniques that can be added to any cascaded speech translation. Specifically, we integrate Mistral-7B1 into our system to enhance it in two ways. Firstly, we refine the ASR outputs by utilizing the N-best lists generated by our system and fine-tuning the LLM to predict the transcript accurately. Secondly, we refine the MT outputs at the document level by fine-tuning the LLM, leveraging both ASR and MT predictions to improve translation quality. We find that integrating the LLM into the ASR and MT systems results in an absolute improvement of 0.3% in Word Error Rate and 0.65% in COMET for tst2019 test set. In challenging test sets with overlapping speakers and background noise, we find that integrating LLM is not beneficial due to poor ASR performance. Here, we use ASR with chunked long-form decoding to improve context usage that may be unavailable when transcribing with Voice Activity Detection segmentation alone.

heading: 1 Introduction
text: This paper provides an overview of Karlsruhe Institute of Technology’s speech translation (ST) system developed for the offline track of IWSLT 2024. We participated in the constrained plus large language models (LLMs) condition, focusing on the translation direction from English to German. Under this condition, LLMs with parameters of around 7 billion are allowed, and they have proven effective in many NLP tasks. One of the interesting aspects of this condition is how one can effectively integrate them into ST systems.
1mistralai/Mistral-7B-Instruct-v0.1
In recent years, there has been a significant interest in developing several open-sourced and medium-scale LLMs (Touvron et al., 2023; Jiang et al., 2023). The adaptability of LLMs to diverse tasks, using techniques such as In-ContextLearning (Brown et al., 2020) or Parameterefficient fine-tuning with 4-bit quantization (Hu et al., 2021; Dettmers et al., 2024), enables their exploitation even with limited resources.
With these recent advancements, exploiting LLMs for ST shows great promise and offers several potential benefits. For instance, one common challenge in Automatic Speech Recognition (ASR) is dealing with input noise, which can often render it difficult to comprehend the speaker’s words. However, LLMs, trained on vast amounts of data, may excel at predicting words compared to decoders trained solely during ASR. Moreover, LLMs possess a richer vocabulary and understanding of complex terminology that task-specific ASR systems may lack. Motivated by these advantages, various studies have explored the integration of LLMs into ASR (Chen et al., 2024; Pu et al., 2023), Machine Translation (MT) (Koneru et al., 2023), and ST (Hu et al., 2024).
Chen et al. (2024) employ the LLM to generate a new hypothesis based on the N-best list of the ASR model. This strategy relies on the observation that N-best lists tend to exhibit enough diversity, especially during uncertain conditions, allowing accurate transcript prediction by examining the list. On the other hand, for MT, Koneru et al. (2023) proposes leveraging the LLM to automatically postedit translations by analyzing the source and hypothesis documents to rectify contextual errors. Both approaches are system-agnostic and have demonstrated successful enhancement of system quality. Furthermore, it is also the case that cascaded systems are shown to be superior than end to end systems in previous IWSLT findings and submissions making the leveraging of LLMs
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easily compatible. (Agarwal et al., 2023; Liu et al., 2023).
Our system builds on these two approaches to effectively use the LLMs to improve the cascaded ST pipeline by refining the intermediate outputs at both ASR and MT while maintaining its modular structure. We utilize pre-trained models to create the individual components and fine-tune them with the allowed data. Specifically, we employ WavLM (Chen et al., 2022) and MBART50 (Liu et al., 2020) to initialize the ASR, and NLLB-200 (3.3B) (Costa-jussà et al., 2022) for the MT module. As for the LLM, we opt for Mistral 7B InstructionTuned (Jiang et al., 2023), considering it to be the most recent model within the allowable options.
We present our main findings below:
• We demonstrate that LLMs can be tailored to enhance both ASR (Section 4.1) and MT systems (Section 4.2), resulting in an absolute improvement of 0.3% in Word Error Rate and 0.65% in COMET, respectively, on the tst2019 test set.
• While we observe significant enhancements in in-domain scenarios, we find that these techniques are not applicable in challenging scenarios (such as Overlapping Speakers, Background noise, etc.) due to poor ASR performance.
• We demonstrate that employing chunked longform decoding2 significantly improves ASR performance in challenging scenarios, such as the case of the ITV dev set. Specifically, we observe a decrease in the word error rate from 37.83% to 30.98%
2We derive the terminology from this blog post.

heading: 2 Data
text: This section describes the evaluation and training data we use in our experiments. For evaluation, we report results on the tst2019 and ACLdev (Salesky et al., 2023) test sets to compare with findings from previous works (Anastasopoulos et al., 2021; Agarwal et al., 2023). We also use the EPTV (European Parlament activities), Itv (TV Series), and Peloton (Fitness TV) dev sets from the subtitling track consisting of overlapping speakers with different accents to evaluate the ASR performance in challenging scenarios.
As the data conditions did not change from IWSLT23 to this year, we rely on the data processed from last year’s submission (KIT’23) (Liu et al., 2023). For the training data of ASR, we use the same system that used Common Voice (Ardila et al., 2020), LibriSpeech (Panayotov et al., 2015), MuST-C v2 (Di Gangi et al., 2019), TED-LIUM v3 (Hernandez et al., 2018), and VoxPopuli (Wang et al., 2021).
While for MT fine-tuning, we use the cleaned training data from last year created from the available parallel data. This includes Europarl v7 and v10 (Koehn, 2005), NewsCommentary v16, OpenSubtitles v2018 (Lison and Tiedemann, 2016), Tatoeba (Tiedemann, 2012), ELRCCORDIS_News and TED2020 (Reimers and Gurevych, 2020) and consists in total of 23 million sentence pairs. For the rest of the paper, we refer to the full parallel data as seed and TED2020 as in-domain.

heading: 3 Overview
text: In this section, we provide an overview of our proposed cascaded system, detailing each individual component. First, the input audio is sent to the ASR system, which undergoes segmentation, and N-best lists are generated for each segmented utter-
ance. Next, the top candidates in the N-best list are fed as input to the LLM, which is trained to refine the ASR output and generate a final ASR hypothesis. Following this, the final ASR hypotheses are passed on to the sentence-level MT system, which produces translations. Finally, the sentence-level automatic transcripts and translations are fed into another adapted LLM, which automatically postedits and generates a coherent document translation of the talk.

heading: 3.1 Automatic Speech Recognition
text: We employed the ASR model from our previous year’s submission (Liu et al., 2023), considering its effectiveness in transcribing the TED domain. For initialization, we utilized WavLM and mBART50 for the encoder and decoder, respectively, before fine-tuning on the ASR data described in Section 2. However, we encountered below-par ASR performance on the challenging sets EPTV, Itv, and Peloton.
We identified several issues that hindered the effectiveness of our ASR model with these sets. Firstly, the model itself was trained on singletalker datasets but inferred with multi-talker noisy datasets, leading to a mismatch in data distribution. Secondly, our typical use of the SHAS model for audio segmentation introduced challenges, as it sometimes missed segmentations and overlooked segments containing human speech.
Data shift is difficult to handle when the training dataset has not changed since last year. We focused more on handling the latter by incorporating longform decoding. The key idea is to better use context (at the text or signal level) for decoding. The long audio file is chunked into smaller segments with a small overlap between adjacent segments. The model is run over each chunk, and the inferred text is joined at the strides by finding the longest common sequence between overlaps.

heading: 3.2 ASR refinement
text: Once we have generated the N-best list, we select the top 5 candidates and utilize an LLM to produce the final hypothesis as shown in Figure 1. In this step, we can adapt the LLM to the task using either few-shot prompting or LoRA fine-tuning techniques. We choose to fine-tune the LLM with adapters based on the findings from (Chen et al., 2024). However, it is crucial to train the LLM under conditions that simulate the test environment, where it should fix errors of our ASR output rather
than on the whisper generated in Chen et al. (2024). To generate the dataset for fine-tuning, we perform inference on our in-domain training data using the gold segmentation. We create pairs comprising the N-best list and the corresponding reference. It is worth noting that we utilized the same data to train the ASR system, which is not ideal. However, resource constraints prevented us from following the augmentation procedure that mitigates this, which we explain further in Section 3.4. Despite this limitation, manual analysis revealed that the ASR did not memorize the training data and produced similar N-best lists to those observed in the test conditions.
Following this, we fine-tuned the Mistral 7B Instruction-tuned LLM (Jiang et al., 2023) using QLoRA (Dettmers et al., 2024), to predict the gold reference based on the top candidates (see the prompt format below). Importantly, we chose not to shuffle the order of the top candidates when providing it in the prompt, as doing so would eliminate the ranking information provided to the LLM, which could be crucial for its performance.
Punctuate and Post-edit the hypothesis based on the predictions: Hyp 1 <SS> Hyp 2 <SS> Hyp 3 .. Post-edited Hypothesis: Gold Reference

heading: 3.3 Machine Translation
text: For building the MT system, we leverage the strong pre-trained model NLLB 200 3.3B (Costa-jussà et al., 2022) that is allowed in the constrained plus LLM track. We perform a two-step fine-tuning approach. Initially, we fine-tune the model on the seed data to adapt it to the spoken language domain. Subsequently, in the second step, we conduct indomain fine-tuning on TED (in-domain) data, given its significance as one of the primary test sets in the offline track. Additionally, we implement checkpoint averaging to improve generalization with the last 3 checkpoints.

heading: 3.3.1 Restoring Punctuations
text: It is important to note that the ASR outputs lack punctuation. Therefore, we conducted experiments with two punctuators. First, we utilized the punctuations generated from the LLM ASR refinement process described in Section 3.2. Second, we employed a DeltaLM-based punctuation model, which was utilized in our previous year’s submission (Liu
et al., 2023). We observed that while the punctuations generated by the LLM were semantically correct, they often resulted in long sequences and led to a degradation in MT performance. As a result, we decided to opt for the second choice and segment the text into sentences using manually crafted rules.

heading: 3.4 Document-level Automatic Post-Editing
text: After translating the individual sentences with the fine-tuned NLLB, the outputs are not coherent as they are translated in isolation. Moreover, any ASR errors that might be fixed by observing the full document will be translated incorrectly. To mitigate this, we perform an additional step of documentlevel automatic post-editing using the source transcripts and sentence translations shown in Figure 2.
Similar to the situation outlined in Section 3.2, we encountered a lack of data for fine-tuning the LLM for document-level post-editing. Hence, we adopted the approach proposed by Koneru et al. (2023) to create the dataset. We divided the indomain TED data into two halves, each containing English audio, English transcript, and German translation. Subsequently, we fine-tuned MT models on each half using the pre-trained models described in Sections 3.1 and 3.3. Following this, we conducted inference using the gold segmentation with our ASR and MT models trained on one half to the other half. This procedure generated a synthetic dataset with noisy ASR input, MT predictions, and corresponding gold references, leveraging the provided segmentation in the data.
We then use the synthetic dataset to create instances of document-level post-editing. We go through each talk and divide the transcripts into
chunks, each chunk containing a maximum of 256 tokens corresponding to the LLM tokenizer. Then for each chunk, we use the transcript, hypothesis and reference to transform them into the format below and train the LLM to predict the gold reference given the noisy transcript and sentence-level hypothesis.
Noisy English Transcript: ASR Hyp 1 <SS> ASR Hyp 2 <SS> .... German Translations:

heading: MT Hyp 1 <SS> MT Hyp 2 <SS> .... Post-Edited German Translations:
text: Ref 1 <SS> Ref 2 <SS> ....
We use the delimiter "<SS>" to align with the input and perform sentence-level evaluation. Then, we again fine-tune the Mistral 7B Instruction-tuned LLM (Jiang et al., 2023) using QLoRA (Dettmers et al., 2024), training it to predict the gold reference given the noisy transcript and translations. We employ the sliding window with payload strategy during decoding as described in Koneru et al. (2023).

heading: 4 Results

heading: 4.1 Automatic Speech Recognition
text: To evaluate the benefit of the additional ASR refinement step described in Section 3.2, we compare the word error rate of our ASR system before and after post-editing, as shown in Table 1. The ASR performance improves in both cases, with a higher absolute improvement observed in the ACLdev set. The LLM is particularly beneficial in the ACLdev set, given that it contains terminology from the scientific domain where the LLM excels. We also observe a relative improvement of 10% in the TED talks, indicating that ASR refinement is beneficial.
However, the performance of the same ASR system on the challenge set was below par. We conducted additional ablation studies and present the results in Table 2 for the challenge dev sets. We compared last year’s ASR system with three conditions: providing gold segmentation, utilizing longform decoding, and using the recently developed Seamless V2 (Barrault et al., 2023).
We observed that providing gold segmentation achieved a score of 16.84, demonstrating its crucial role in handling this challenging set for EPTV. Moreover, long-form decoding significantly narrowed the gap, decreasing the word error rate for both EPTV and ITV. Meanwhile, our ASR shows the best performance for Peloton without any modifications. Additionally, we evaluated Seamless to assess its robustness and found that its performance was severely lacking in comparison.
Based on these results, we use the ASR with standard segmentation for TED and Peloton test sets. For EPTV and ITV, we use the ASR system with long-form decoding. We found that the LLM cannot refine the N-best list given the poor WER of KIT’23 ASR for the latter test sets and generates long sequences with repetitions for most utterances. Therefore, we do not perform any ASR or MT LLM refinements for ITV and EPTV sets and generate translations with a standard cascaded ST pipeline.

heading: 4.2 Cascaded Speech Translation
text: In this section, we evaluate the final quality of our cascaded ST using the mwerSegmenter to realign the hypothesis with the reference segmentation. We
report results with BLEU (Papineni et al., 2002) and Chrf2 (Popović, 2015) computed by Sacrebleu (Post, 2018). We also report the COMET (Rei et al., 2022) score using the default model3.

heading: 4.3 Two-step Fine-tuning
text: We presented a two-step fine-tuning approach to adapt our MT system in Section 3.3 to the target domain. We report the translation quality on tst2019 test set with this approach (last row) and other models for comparison in Table 3.
Firstly, we observe that Seamless performs inferiorly to NLLB across all translation metrics. Consequently, we proceeded with NLLB for further experiments.
Subsequently, fine-tuning the seed parallel data improved quality across all metrics, notably in-
3Unbabel/wmt22-comet-da
creasing the score from 77.41 to 77.87 in COMET. Following this, with the assistance of second-step fine-tuning, we observed further improvements, resulting in scores reaching 78.49. However, it is important to note that this system still lags behind last year’s submission, which was specifically adapted to the TED domain. Nevertheless, it’s worth highlighting that this system was trained across multiple languages, placing it in the unconstrained condition for IWSLT24. Moreover, we could not replicate a similar adaptation process for NLLB due to resource and time constraints.

heading: 4.4 LLM Refinement
text: We proposed improving the ASR outputs and converting sentence-level to document-level translations using fine-tuned LLMs. We evaluate the benefits of the individual steps and report the results of our final cascaded ST system in Table 4 on tst2019 and ACLdev2023 test sets.
First, the benefits of two-step fine-tuning, ASR refinement, and document post-editing complement each other. Using KITs 23 ASR with NLLB 3.3 B as a baseline, we obtained 77.41 COMET in tst2019 test set. However, including all enhancements led to a total improvement of 2.23 COMET points. Furthermore, the improvements are consistent with both lexical and neural metrics.
Next, we observed that integrating LLMs provides significant benefits in the ACLdev set compared to the TED dev sets. This is plausible due to scientific terminology and accented speakers in the ACLdev set. Both of these challenges are well-suited for LLMs, as the quality of the initial systems is sufficient to utilize context and rectify mistakes reliably.

heading: 5 Conclusion
text: This system paper presented KIT’s submission for the offline track in the constrained + LLMs condition, focusing on the English-to-German translation direction. Using modular techniques, we successfully integrated LLMs into any cascaded ST pipeline. Additionally, we highlighted the benefits of long-form decoding in scenarios involving noisy and overlapping speech.
For future work, we aim to explore robust techniques for integrating LLMs that can effectively handle challenging scenarios where ASR quality is sub-par. Furthermore, the translation’s latency is quite high as it needs to call the LLM twice. However, integrating quality estimation techniques to decide when we need the LLM can limit the effects of the high latency problem.

heading: Acknowledgments
text: This work is partly supported by the Helmholtz Programme-oriented Funding, with project number 46.24.01, named AI for Language Technologies, funding from the pilot program Core-Informatics of the Helmholtz Association (HGF). It also received partial support from the European Union’s Horizon research and innovation programme under grant agreement No 101135798, project Meetween (My Personal AI Mediator for Virtual MEETtings BetWEEN People). The work was partly performed on the HoreKa supercomputer funded by the Ministry of Science, Research and the Arts BadenWürttemberg and by the Federal Ministry of Education and Research.

heading: A Appendix
text: We use the transformers library (Wolf et al., 2019) for fine-tuning our ASR and LLM and the fairseq toolkit (Ott et al., 2019) for fine-tuning NLLB 3.3B. For the ASR training, we set the batch size to 384, resulting in approximately 128 minutes per batch. We employ a warmup strategy over 2, 000 steps and a total of 100, 000 training steps. The learning rate is initialized to 1e− 4.
For the NLLB fine-tuning experiments, we use a learning rate set to 5e− 5, label smoothing to 0.1, drop out to 0.1, attention drop-out to 0.1. We use the Adam optimizer with betas to (0, 9, 0.98) and the remaining optimizer parameters to default. We used a batch size of maximum 3096 making one step, update-freq to 16 and validating on the dev
set after every epoch. We stopped the training after the dev loss did not increase after 10 epochs. For fine-tuning the LLM with QLoRA we use the peft (Mangrulkar et al., 2022) along with the transformers library. We add LoRA adapters to the target modules [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj]. We set the adapter rank to 16, alpha to 32 and lora dropout to 0.1. We use a batch size of 8, learning rate of 5e− 5 with other parameters set to default. After every 200 steps, we validate and terminate the training if it does not improve 10 consecutive times.
During inference, we use beam search for all ASR, MT and LLM components. The ASR and MT decode with beam size of 5, whereas the LLM does it with beam size of 3.

heading: Future Work
text: For future work, we aim to explore robust techniques for integrating LLMs that can effectively handle challenging scenarios where ASR quality is sub-par. Furthermore, the translation’s latency is quite high as it needs to call the LLM twice. However, integrating quality estimation techniques to decide when we need the LLM can limit the effects of the high latency problem