Language understanding in speech-based systems have attracted much attention in recent years with the growing demand for voice interface applications and devices such as Alexa Wang et al. (2020a), Siri Williams and Young (2007), and Cortana Wang et al. (2018b)
. These speech-based intelligent systems usually comprise an automatic speech recognition (ASR) component which converts audio signals to readable natural language text, and a natural language understanding (NLU) component which takes the output of ASR component as input and fulfill downstream tasks such as sentiment analysis, natural language inference, and response selection. The upstream ASR error may propagate to the downstream NLU component and degrade the overall performanceSerdyuk et al. (2018); Wang et al. (2020b). In real-world scenarios, ASR error can be ubiquitous due to poor articulation and acoustic variability caused by environment noise and reverberation Errattahi et al. (2018). The persistence of ASR error indicates a need for ASR-robust natural language understanding.
Previous work in this area is limited to task-oriented language understanding such as hotel reservation and meeting scheduling through human-machine interactions Schumann and Angkititrakul (2018); Weng et al. (2020); Rao et al. (2020); Huang and Chen (2020). However, ASR error can affect many other NLU tasks, such as sentiment analysis in voice assistants. A benchmark that enables the comprehensive evaluation of NLU under ASR error on a diverse range of tasks is still missing.
In this paper, to quantitatively investigate how ASR error affect NLU capability, we propose the ASR-robust General Language Understanding Evaluation (ASR-GLUE
) benchmark: a collection of 6 NLU tasks including sentiment analysis, similarity and paraphrase tasks and natural language inference (NLI) tasks. We hire 6 native speakers to convert the test data into audio recordings with 3 different levels of environment noise. Each speaker is requested to record all test data to study the variance between individuals. Finally we get 18 different types of audio recordings (3 levels of noise * 6 different speakers) for each of the 6 NLU tasks, varying in noise intensity, error type and speaker variants. In addition, we also test the human performance under different noise levels. We hope it would benefit the research of ASR-robust NLU in the future.
With the annotated dataset above, we observe that recent state-of-the-art (SOTA) NLU models are sensitive to ASR error. To alleviate this problem , a straightforward approach is to make error correction for the mistranscriptions as a post-processing step. Another approach is data augmentation which uses text with potential ASR error as additional training data to simulate the real-world scenario in training. We propose two different augmentation methods: 1) Audio-level augmentation. We adopt a Text-to-Speech system (TTS) - then - ASR pipeline to first transforms the training corpus to audio and then convert them back to text (with errors). 2) Text-level augmentation. Due to the high cost of TTS and ASR system, we further attempt to injects the ASR error to the training corpus through text generation modelsRadford et al. (2019); Lewis et al. (2019) or some manually predefined rules Fazel-Zarandi et al. (2019); Schatzmann et al. (2007). Experimental results demonstrate that while these approaches improve the model robustness against ASR error to some extent, they are still far from human understanding capability.
Our contributions are as follows: 1) A new benchmark dataset, ASR-GLUE, is proposed to enable a comprehensive evaluation of the robustness of NLU model to ASR error, covering 6 diversified tasks under 6 different speakers and 3 different levels of environment noise. 2) We systematically and quantitatively investigate the sensitivity of state-of-the-art NLU models to ASR error in terms of noise intensity, error type and speaker variants. 3) We provide two types of approaches to alleviate the effect of ASR error. Experimental results demonstrate their effectiveness, and they can be considered as baseline methods in future studies.
2.1 Selected NLU Tasks
The proposed ASR-GLUE is constructed on the basis of GLUE Wang et al. (2018a), a popular NLU evaluation benchmark consists of diverse NLU tasks. We select 5 typical NLU tasks from it, namely: Sentiment classification (SST-2 Socher et al. (2013)), Semantic Textual Similarity (STS-B Cer et al. (2017)), paraphrase (QQP 222data.quora.com/First-Quora-Dataset-Release-Question-Pairs
), Question-answering NLI (QNLIRajpurkar et al. (2016)
), Recognizing Textual Entailment (RTEDagan et al. (2005); Haim et al. (2006); Giampiccolo et al. (2007); Bentivogli et al. (2009).) and incorporate with a Science NLI task (SciTail Khot et al. (2018)), resulting in 6 tasks in total. Detailed descriptions of the the chosen tasks are presented in Appendix. They are common and typical tasks of language understanding in speech-based scenarios, making them suitable for ASR-GLUE.
2.2 Data Construction
Since the original datasets are presented in clean text form, we manually select instances from their test sets for human audio recording to evaluate the NLU capability in the presence of ASR error. Samples with non-standard words (e.g., abbreviations, currency expressions, strange names and addresses) or sentences that are too long (more than 100 words) are excluded from these selected test sets. Considering the cost and quality of annotation, we keep the original training set and randomly select a subset of samples from the test set for human audio recording on each task333If there is no public test set, we use their dev set instead.. The statistics of the data is shown in Table 1. In human recording process , 6 native speakers are hired to record all test samples. Different levels of environment noise is provided and the audio signals are sent into an ASR system to get the final ASR hypothesis. The overall process is depicted in Figure 1.
|Low Noise||Medium Noise||High Noise||Test+Dev|
Human Audio Recording
We hire six native speakers to record the test sets for each task. The speakers are 3 male and 3 female with different ages ranging from 18 to 40 from the U.S. In the recording process, each speaker is required to record all text samples independently so that we can study the impact of speaker variation. They are instructed to imagine they are communicating with someone when speaking the sentences of the six tasks. Note that they are allowed to make minor changes to the original text for natural and smooth expression such as change cannot to can’t. To collect high-quality audio, all the original speech signals are recorded in a low-noise environment.
In real-world scenario, human audio often recorded with environment noise and reverberation Wölfel and McDonough (2009). The presence of the background interference will lead to substantial performance degradation of current ASR systems Barker et al. (2018) and further effect the downstream NLU systems Henderson et al. (2014). Therefore, to better evaluate the robustness of NLU models in the noisy acoustic environment, speech data with different levels of noise is further provided in the ASR-GLUE corpus.
In ASR-GLUE, the widely-adopted simulation approach Ko et al. (2017)
is used to introduce different levels of noise and reverberation into the low-noise audio signals. Specifically, the background noise caused by such as phone ring, alarm clock and incoming vehicles are randomly sampled and added into the original recordings with the signal-to-noise-ratio (SNR) from 10dB to 15dB. Here SNR is defined as:, where and denote the acoustic signal of the clean speech and the noise respectively. denotes the signal power. In addition, the room reverberation is also introduced by involving the recorded audio signals with the Room Impulse Responses (RIRs) 444The noise and RIR files can be found via link: http://www.openslr.org/resources/28/rirs_noises.zip generated by the image-source method Habets (2006). The simulation process totally covers 843 kinds of different background noise and 417 types of different RIRs.
Finally, for each recorded human audio signal we get three versions: (1) Low-level noise, same as the original audio. (2) Medium-level noise which introduces reverberation and 15dB SNR level background noise into the original audio. (3) High-level noise , which introduces reverberation and 10dB SNR level background noise into the original audio. Then we build an 6000h trained ASR system based on the widely-used open source Kaldi toolkit555https://github.com/kaldi-asr/kaldi Povey et al. (2011, 2016) to transcribe these audio files into text.
3 Analysis of Asr-Glue
In this section we give extensive analyses on the proposed ASR-GLUE dataset. In Section 3.1, we obtain human performance to measure the ceiling performance on the test set, Then we analyse the performance of recent SOTA NLU models across different levels of environment noise in Section 3.2. Furthermore, in Section 3.3 we categorize ASR errors into four types, and systematically investigate the impact of different error types on NLU performance. Finally, we analyse the effect of voice variation from different speakers in Section 3.4.
3.1 Human Performance
To obtain the human performance under environment noise, we hire native annotators to predict labels of each test sample in audio form. The annotators is hired from a third-party company. To guarantee the annotators fully understand these tasks, we first give the annotators a brief training on each task in ASR-GLUE. Then we ask them to take an exam before starting annotation, only annotators who pass the exam will be hired. Finally we have 3 annotators to measure the ceiling performance for each task in ASR-GLUE. Details about the exam and annotation process are presented in Appendix.
3.2 Performance of Existing NLU Models
To demonstrate the significance of the ASR error issue, we leverage typical NLU models such as BERT to test their robustness to different levels of ASR error on different tasks. As shown in Figure 2(a), While BERT yields promising results on error-free text, its performance degrades in presence of ASR error on six tasks. As the noise increases, the performance of the model drops more severely. In contrast, human is less affected by the environment noise, which indicates that there still remains a gap between the robustness of models and humans to ASR error.
We also investigate the effect of ASR error with different noise levels on different NLU models. We adopt base version of BERT Devlin et al. (2018), RoBERTa Liu et al. (2019), ALBERT Lan et al. (2019), XLNet Yang et al. (2019) as the NLU model. As shown in Fig. 2(b) we can observe that all these pretrained language models are sensitive to ASR error and the performance degrades with the increase of the noise level.
3.3 Breakdown Analysis by ASR Error Type
|Error Type||Ground Truth||Recognition Result|
|Similar sounds||The man couldn’t lift his son.||The man couldn’t lived his son.|
|Tommy dropped his ice cream.||Tommy jumped his ice cream.|
|Liaison||Does Quora stand for question or answer.||Does Quora Stanford question or answer.|
|The drain is clogged with hair.||The drains clogged with hair.|
|Insertion||This afternoon.||This after afternoon.|
|A warm funny engaging film||A warm funny and engaging film.|
|Deletion||A black and white photo of an old train station.||A black white of train station|
|Old style bicycle parked on floor||Old style bicycle floor|
We conclude that the ASR error types can be categorized into four folds, namely similar sounds, liaison, insertion and deletion. Specifically, (i) Similar sounds happen when ASR system sometimes wrongly identifies one word as another with similar pronunciation. (ii) Liaison constantly occurs between successive words which has sound fusion across word boundaries. (iii) Insertion happens when ASR system makes word redundancies. (iv) Deletion happens when there are word omissions in the ASR hypothesis. Examples of each error type are presented in Table 2.
We report the percentage of each aforementioned error type in Figure 3(a). We choose SST-2 task as an example and observe that similar sounds most commonly happened. As the noise increases, the percentage of similar sounds and deletion made by ASR system gradually increase while the percentage of liaison and insertion error type remain relatively stable. Note that the sum percentages of the four error type is over 100%, since different error types may simultaneously exist in one hypothesis.
We also investigate the impact of each error type on NLU model on SST-2 task. We group data according to error types and compare the model performance with the same grouped raw data without errors respectively. As shown in Figure 3 (b), we can observe that BERT can better handle Liaison error for the performance degradation is minimal under varied noise settings. As noise increases, the accuracy of the model decreases more severely for each different type of error.
3.4 Effect of Individual Difference
We test the effect of voice variation of the six hired speakers on the test set of SST-2. As shown in Fig. 4 we can observe that the recognition quality and classification accuracy varies greatly across speakers. For example, the accuracy of BERT on S1 (corresponds to Speaker 1) are consistently higher than S6 (corresponding to Speaker 6) with a very large margin (). Meanwhile, we can also observe that the drop of classification accuracy is due to the increase of WER. The higher WER means more ASR errors in the test samples, resulting in more misclassification.
4 ASR-Robust NLU
To alleviate the effect of ASR error to NLU models, we propose two strategies: correction-based methods and augmentation-based methods. As shown in Fig 5, the first strategy (Section 4.1) is to recover the clean text from erroneous text while the second aims to train NLU models on data augmented with ASR error. In the second strategy, there are two types of augmentation method: 1) audio-level augmentation (Section 4.2), we adopt a TTS-ASR pipeline which first transform the training corpus into audio files then convert them into text containing ASR error. 2) Text-level augmentation (Section 4.3): due to the high cost and latency time of TTS and ASR system, we propose to use generation models Radford et al. (2019); Lewis et al. (2019) or some manually predefined rules Fazel-Zarandi et al. (2019); Schatzmann et al. (2007) as a substitution of the TTS-ASR pipeline to direct generate text with ASR error from clean text.
4.1 ASR Correction
ASR error correction is a ASR post-processing process that recover the clean text from erroneous text. We adopt two most typical technical paradigms: GETToR Omelianchuk et al. (2020) and BART Lewis et al. (2019) (referred to as BART-C) to transform the output of the ASR systems into clean text.
In training, the model takes the ASR hypothesis as input and its corresponding clean transcript as output. In this way model learns to correct the errors in the hypothesis and recover it to a clean sentence. During inference time, the input of NLU models will be the output of ASR error correction model other than the original ASR hypothesis.
4.2 Audio-Level Augmentation
An illustration of the proposed two strategy: ASR error correction and data augmentation. (a) is an example of ASR correction which corrects the source erroneous input “hello word” to target correct output “hello world”. (b) is an example for data augmentation. The source input is clean text data (e.g., “hello world”) and the target output is noisy data with ASR error (e.g., “hello word”). We simulate ASR error in audio or text level and train the NLU models with the augmented data.
We also attempt to train the NLU models with ASR error so that they can learn to understand those text containing ASR error during inference time. One straightforward way is to hire human speakers to record all training data to audio files. However it is unpractical due to high labor cost. Thus, as shown in the upper part of Figure 5(b), in audio-level augmentation we adopt a TTS system to convert the text-form training data in into audio files. Then we add random environment noise into the audio and adopt ASR systems to convert the audio files into ASR hypotheses. Specifically, the TTS system we adopted is DurIAN Yu et al. (2020). The noise simulation and speech recognition follows the same process as described in section 2.2. In training we use these augmented data as additional training data, along with the original training set to train the NLU model. Note that the original training data set is important and cannot be discarded in training to make the model maintain its original performance on clean text.
4.3 Text-Level Augmentation
In audio-level augmentation, the audio from TTS system is still quite different from human voice, which may cause a different error distribution. Besides, the TTS-then-ASR pipeline is time-consuming and expensive, since it takes one week to generate the synthesized audio signals using four 32-core Intel E5 CPU servers and another week to transcribe them with three 2-card Tesla P4 GPU servers using the kaldi-based ASR system. So we propose text-level augmentation which uses text generation models as a substitution of the TTS-ASR pipeline and directly generate the ASR hypothesis from the original textual training data. These generation models take the ASR transcript as the model input and train model to generate the corresponding hypothesis as output.
) are used as the generation models to generate ASR hypotheses. We also adopt a rule-based method based on Confusion-matrix (abbreviated asCM) Schatzmann et al. (2007)
). In CM, each ASR hypothesis and its reference text are aligned at the word level by minimizing the Levenshtein distance between them. Then we conduct the confusion matrix based on the aligned n-grams and add ASR error according to the frequencies of confusions. Further comparisons about the aforementioned methods are presented in experiments.
5.1 Experimental Settings
The clean transcriptions and their corresponding ASR hypotheses which are used to train the correction model and augmentation models are collected from the public dataset LibriSpeech Panayotov et al. (2015). LibriSpeech is an ASR corpus based on public domain audio books which contains 1000 hours of speech and their according transcriptions. We use the Kaldi-based ASR system to convert audio recordings into ASR hypothesis. For correction-based methods, the input is hypothesis and the output is its corresponding clean transcript. For text-level augmentation methods, the input is the clean transcript while the output is its corresponding hypothesis which contains ASR error. In text-level and audio-level augmentation, the proportion between the augmented data and original data is 1:1 and we combine them together as the training set.
The NLU model we used in our experiment is 666https://huggingface.co/bert-base-uncased. We use Adam Kingma and Ba (2014) with an initial learning rate of 5e-5. For GPT-2777https://huggingface.co/gpt2 and BART888https://huggingface.co/facebook/bart-large/tree/main used in text-level augmentation (BART-S) and ASR correction (BART-C), sentences are tokenized with byte-pair encoding (BPE) Sennrich et al. (2015). Both of them use beam search Koehn (2004) as their decoding strategy. For GETToR, the sequence tagging model is an encoder made up of RoBERTa Liu et al. (2019)
The main result of proposed methods are shown in Table 3, we can observe that the NLU models are sensitive to ASR error and the performance of NLU degrade severely across various task. The proposed methods can effectively improve the robustness of the model to a certain extent in most scenarios, but are still far from human performance. For example, the accuracy on SciTail task under high environment noise decline by 32.35% and is restored from 62.39% to 78.62% by audio-level augmentation method. In contrast, human are almost unaffected by the ASR error and still maintain high accuracy of 90.13%. Moreover, the human performance is more stable than the NLU model across all noise-levels for various tasks. We can also observe that although audio-level augmentation method achieves promising results on most tasks, it is even worse than the original BERT on RTE task under high environment noise (52.73% vs 52.86%).
We also find that the performance of different method varies across tasks, and it is difficult to find a universal method for all scenarios. Although the audio-level data augmentation method always gains the highest accuracy on the data with ASR errors, it can not maintain the original performance on clean data for certain tasks, e.g., worse than original BERT on clean text data (90.79% vs 94.74 on SciTail, 87.42% vs 90.73% on QNLI, and 63.76% vs 68.93% on RTE).
Another interesting phenomena is that the ASR error correction is less effective in many cases. On half of the tasks, such as SST-2, STS-B, QNLI, BERT with correction performs similar or even worse than the original one. On other tasks, although it performs better than original BERT, but still worse than augmentation-based methods. One possible reason for its poor performance is that the ASR system already integrates a strong n-gram language model to guarantee the quality of system output. So an additional language model is redundant and cannot make further improvement.
By comparison on various tasks, we can observe that the text-level augmentation achieves more promising results than other methods. It works well in most situations, without degrading the NLU performance on clean text in general. We further make comparisons between the three text-level augmentation methods and find that none have absolute advantages. GPT-2 based augmentation performs better in most situations, but on RTE task with clean text, the accuracy is much lower than original BERT (64.69% vs 68.93%).
Overall, although the proposed methods can improve the model robustness to some extent, a large gap still remains between these approaches and human, which indicates much potential for work on robustness improvement for the NLU model.
5.3 Effect of ASR systems.
To verify this assumption that the ASR error may no longer be a serious problem in NLU for a good enough ASR system, we conduct a further experiment that replace our kaldi-based ASR system with a SOTA public ASR system: Google ASR. We replace the ASR system with Google ASR on dev and test sets (not on training set due to its high price) and test the performance of BERT on these new sets.
As shown in the left part of Figure 6, we report the performance of vanilla BERT on STS-B for two different speakers (Speaker 1 and Speaker 6). The performance of Speaker 1 seems consistent with the previous assumption that a good ASR system solves this problem. Nevertheless, for speaker 6, we observe an opposite phenomena that the model performance still drops a lot (about 15%) in presence of ASR error. To investigate the reason for these differences, we carefully compared their audio, and find that although these both of them are native speakers from America, speaker 6 holds a strong regional accent which can significantly increase the ASR error. Since the accent are widely existed in audio signals, it is unrealistic to assume the input audio are always clean and standard. So even if we have a good ASR system, strong and robust NLU models are still necessary.
We also report the result of the proposed methods with Google ASR in the right part of Figure 6. On STS-B task, the performance of most methods are consistent with the results in Table 3. We can observe the most data augmentation-based methods still perform well while the correction-based methods are less effective. Although in training these methods are based on another ASR system, they can generalize to Google ASR systems as well, which proves the generalization ability of augmentation-based methods.
6 Related Work
Many benchmark datasets are created to facilitate Spoken Language Understanding (SLU) Coucke et al. (2018); Lugosch et al. (2019); Wang et al. (2020b); Price (1990); Henderson et al. (2014); Peng et al. (2020), which evaluate the robustness of the downstream NLU model against the error output from the upstream acoustic model Schumann and Angkititrakul (2018); Weng et al. (2020); Rao et al. (2020); Huang and Chen (2020). However, they are only designed for a particular domain or a specific task such as intent detection and slot filling. In contrast, the human ability to understand language is general, flexible, and robust. There is a need to test general-purpose natural language understanding capability on diverse tasks in different domains.
Large-scale pretrained language models have achieved striking performance on NLU in recent years Jin et al. (2020); Yang et al. (2019). Recently many works test their robustness by human-crafted adversarial examples Nie et al. (2019) or generated examples by adversarial attacks Jin et al. (2020); Madry et al. (2017); Zhu et al. (2019); Dong et al. (2021). Zhao et al. (2018)
projects the input data to a latent space by generative adversarial networks (GANs), and then retrieves adversaries close to the original instance in the latent space.Iyyer et al. (2018) propose controlled paraphrase networks to generate syntactically adversarial examples that both fool pre-trained models and improve the robustness of these models to syntactic variation when used to augment their training data. However, the robustness of pre-trained model to speech recognition error in real conditions has not been fully explored.
We present ASR-GLUE, a new benchmark for evaluating general-purpose language understanding under ASR error in speech-based applications. We propose two ways to improve robustness of the NLU system and find that there is still a gap between the NLU capability of the model and humans. ASR-GLUE offers a rich and challenging testbed for work developing ASR robust model for general-purpose language understanding. Given the difficulty of ASR-GLUE, we expect that further progress in multi-task, multi-model learning techniques will be necessary to approach human-level performance on the benchmark.
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Appendix A Appendix
a.1 Detailed descriptions for the Asr-Glue datasets
SST-2 The Stanford Sentiment Treebank Socher et al. (2013) is a single-input understanding task for sentiment classification. The task is to predict the sentiment of a given sentence in movie reviews domain. Accuracy (ACC) of the binary classification (positive or negative) is used as the metric.
STS-B The Semantic Textual Similarity Benchmark Cer et al. (2017) consists of sentence pairs drawn from news headlines, video and image captions, and natural language inference data. The task is to predict sentence similarity scores which ranges from 1 to 5. We evaluate using Pearson and Spearman correlation coefficients.
QQP999 data.quora.com/First-Quora-Dataset-Release-Question-Pairs dataset consists of question pairs in social QA questions domain. The task is to determine whether a pair of questions are semantically equivalent. Accuracy (ACC) is used as the metric.
Question-answering NLI is modified from the Stanford Question Answering datasetRajpurkar et al. (2016). This is a sentence pair classification task which determines whether the context sentence contains the answer to the question. Accuracy (ACC) is used as the metric.
SciTail SciTail Khot et al. (2018) is a recently released challenging textual entailment dataset collected from the science domain. This is a natural language inference task which determines if a natural language hypothesis can be justifiably inferred from a given premise. Accuracy (ACC) is used as the metric.
RTE The Recognizing Textual Entailment (RTE) datasets come from a series of annual textual entailment challenges merged from a collection of Dagan et al. (2005); Haim et al. (2006) All datasets are combined and converted to two-class classification: entailment and not entailment. Accuracy (ACC) is used as the metric.
a.2 Details of kaldi-based ASR word error rate results
a.3 Details for the exam and annotation process
In the annotation process, the annotators are required to predict labels of each test sample according to the corresponding audio signals. Note that for each test sample we have 18 audio signals (3 levels of noise * 6 speakers), which is a big burden for the annotators. So we randomly select one audio from the six speakers with each noise level and report the averaged performance of the annotators on all tasks.
The exam is set to guarantee the annotators fully understand each task. In the exam, we randomly select 50 samples from the original datasets which is in text form for each task. Annotators who achieve at least 90% accuracy on these samples will be hired.