Venue: NAACL 2024

# Encoding of lexical tone in self-supervised models of spoken language

 Gaofei Shen\({}^{1}\) Michaela Watkins\({}^{2}\) Afra Alishahi\({}^{1}\) Arianna Bisazza\({}^{3}\) Grzegorz Chrupala\({}^{1}\)

\({}^{1}\) Tilburg University \({}^{2}\) University of Amsterdam \({}^{3}\) University of Groningen

{g.shen, a.alishahij@tilburguniversity.edu

m.m.watkins@uva.nl a.bisazza@rug.nl

grzegorz@chrupala.me

###### Abstract

Interpretability research has shown that self-supervised Spoken Language Models (SLMs) encode a wide variety of features in human speech from the acoustic, phonetic, phonological, syntactic and semantic levels, to speaker characteristics. The bulk of prior research on representations of phonology has focused on segmental features such as phonemes; the encoding of suprasegmental phonology (such as tone and stress patterns) in SLMs is not yet well understood. Tone is a suprasegmental feature that is present in more than half of the world's languages. This paper aims to analyze the tone encoding capabilities of SLMs, using Mandarin and Vietnamese as case studies. We show that SLMs encode lexical tone to a significant degree even when they are trained on data from non-tonal languages. We further find that SLMs behave similarly to native and non-native human participants in tone and consonant perception studies, but they do not follow the same developmental trajectory.

+
Footnote †: _Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)_, pages 4250–4261

June 16-21, 2024 ©2024 Association for Computational Linguistics

## 1 Introduction

Explaining the inner workings of self-supervised models of written and spoken language has been the focus of much recent work. Transformer-based Vaswani et al. (2017) written language models have been shown to encode many types of linguistic information Conneau et al. (2018); Hewitt and Manning (2019). The analysis of self-supervised Spoken Language Models (SLMs) is also gaining traction: architectures such as wav2vec2 Baevski et al. (2020) and HuBERT Hsu et al. (2021) have been shown to encode linguistic information at the phonetic, phonological, syntactic and semantic levels of human speech without labeled data Abdullah et al. (2021); Ma et al. (2021); de Seyssel et al. (2022); Bartelds et al. (2022); Martin et al. (2023); Shen et al. (2023); Pasad et al. (2024).

The majority of research on representations of phonetic and phonological information in SLMs focuses on the segmental level. **Segmental** refers to units of speech that do not spread but remain localized. Phonemes (e.g. vowels and consonants) are the smallest abstract units of sound that help to distinguish one unit from another (e.g. **pat vs bat**). **Suprassegmental**, in contrast, refers to features that are not necessarily limited to single units, but can spread across multiple phonemes or phrases. Examples include tone, stress patterns, and intonation, which can all entail syllable and phrase level changes Singh and Fu (2016). The representation of suprasegmental information in SLMs is important to study, as it is one of the main distinguishing features of speech compared to text: spoken utterances use suprasegmental cues to convey information that is sometimes not explicitly marked in a corresponding written sentence.1 As a first step, in this work, we focus on lexical tone as a highly constrained, relatively well-understood example of a suprasegmental feature.

Footnote 1: Mandarin does not explicitly mark tone in writing, but Vietnamese does.

We firstly examine to what extent SLMs trained on tonal and non-tonal languages encode tone information in their internal representations. We find that SLMs are capable of capturing tonal information, regardless of whether they are trained on tonal or non-tonal languages.

Secondly, we investigate the impact of supervised fine-tuning on the automatic speech recognition (ASR) task. We find that fine-tuning _enhances_ tone representations for models trained on tonal languages, but _reduces_ them for models trained on non-tonal languages.

Thirdly, we explore whether SLMs exhibit the same perceptual patterns as native and non-native human listeners. We find that models show patterns similar to humans in discrimination of Mandarin tones and consonants, but find no evidence that they follow a similar developmental trajectory.



Tones

Estimates suggest that more than 60% of the world's languages use some degree of tonal contrast Yip (2002). The primary focus of this work is on lexical tone, the process by which lexical items are distinguished from one another primarily by pitch cues Chen et al. (2022). We focus primarily on pitch cues: while there are suggestions that articulatory cues alone are not sufficient for tonal distinction in languages such as Vietnamese, with socio-linguistic register also being important if present in the language in question Brunelle (2009), this is still subject to further research in perception and production studies to validate as the body of work remains small, particularly regarding perception. We therefore state here that non-tonal phonemic units (e.g. vowels, consonants) can be defined primarily by **non-pitch articulatory cues**, such as vowel height, voicing, and duration. In contrast, tonal units make use of **pitch cues**, with F0 (fundamental frequency) contour usually considered to be the primary cue Rhee et al. (2021). In ambiguous contexts, other pitch cues can be used in combination with non-pitch cues such as amplitude, voice quality (e.g. breathy vs creaky), and spectral tilt Rhee et al. (2021).

The Tone Bearing Unit (TBU) is the segment containing tone; this is typically, although not always, found on the syllable level. In the case of the primary language investigated in this paper, Mandarin, syllables are morpheme based (as in, Mandarin syllables _are_ morphemes). Thus in Mandarin every morpheme contains obligatory tone, and is in of itself the TBU Jun and Kubozono (2020).

We compare SLMs trained on non-tonal languages as well as three fully lexical tonal languages: Mandarin, Cantonese and Vietnamese. The models are tested primarily on Mandarin data. Mandarin demonstrates full tonality, with tone found on each morpheme Hyman (2018), and has been widely studied for tone perception as well as acquisition. Secondarily, we also test on data from another lexical tone language, Vietnamese, to assess if our results generalize.

**Mandarin Chinese** is typically described as containing four (lexical) tones and one neutral tone that only occurs in unstressed syllables Wu et al. (2020). The tones are conventionally assigned the labels 1-4 (T1-4); Figure 1 illustrates the four Mandarin tones.

Since one morpheme (one character) corresponds to one tone in Mandarin Chinese, we can use the Pinyin transcription to obtain our tone labels easily (see Figure 1 for notations); for example:

\begin{tabular}{l l}  & \\  & \\  & Jin tan qi hen hao \\  & TI T1 T1 T4 T3 T3 \\  & ‘The weather today is very good.’ \\ \end{tabular}

The tone label corresponds to the tone of the character when it is pronounced in isolation (base form). However, Mandarin features _tone sandhi_, i.e. the tone assigned to individual morphemes can change in pronunciation based on the tone of the adjacent morpheme (sandhi form). One instance of tone sandhi rules in Mandarin is T3 sandhi Chen (2000): if two T3 (‘dipping') tones occur next to one another, the first will adjust to T2 ('rising') to avoid two consecutive T3 tones, as can be seen in examples 2 and 3, after Chen (2000). Tone labels obtained from Pinyin transcriptions only take the base form into account.

\begin{tabular}{l l l}  & & /\backslash \\  & xiao & \\  & T3 & \\  & ’small’ & \\  & & \\  & xiao & gou \\  & T3 & T3 & _base form_ \\  & T2 & T3 & _sandhi form_ \\  & ‘small & dog’ \\ \end{tabular}

Figure 1: F0 contours of the four Mandarin tones measured from pronunciations recorded by one of the co-authors, a native speaker of Mandarin Chinese. The four syllables are pronounced in isolation (notation: m̄ T1, m̄ T2, m̄ T3, mÅ T4).

 

The primary pitch cue that distinguishes the individual Mandarin tones from each other is F0; however, secondary pitch cues are also present such as voice quality and spectral tilt (Belotel-Grenie and Grenie, 1994; Huang, 2020).

**Vietnamese** also has obligatory tones on every syllable, similar to Mandarin's morphometric TBU (Kirby, 2011). We adhere to the eight tone system described by Kirby (2011) in our experiment setup.

**Cantonese** is a Sinnitie language related to Mandarin, and also features lexical tone, with six tonal distinctions (Zee, 1991) as opposed to Mandarin's four.

## 3 Related work

The present paper builds both on works interpreting the inner workings of SLMs and on experiments on perception of aspects of human speech.

### Analyzing SLMs

The transformer architecture (Vaswani et al., 2017) has dominated the SLM realm. Researchers have developed many methods to analyze the innerworking of these models. Pasad et al. (2021) provide an overview on the variety of linguistic featurs encoded by self-supervised SLMs. The models tend to follow an autoencoder-like behavior with the middle layers showing the strongest encoding of a variety of linguistic features.

More recently, research has focused on specific properties of the input audio that is being encoded by the models. Martin et al. (2023) tested whether SLMs can distinguish between voiced and voiceless consonants. Shen et al. (2023) showed that self-supervised as well as visually-supervised SLMs are capable of encoding syntactic properties to some extent. Some prior works in the field have touched on the encoding of suprasegmental features in SSL speech models. Bartelds et al. (2022) showed the hidden state activations of SLMs are capable of capturing intonational and durational information on the phrase level, indicating that they can encode non-segmental information to a significant degree.

Many recent interpretability studies are inspired by psycholinguistics and child language development research. With the rise of probing and other interpretability methods, researchers replicated experimental paradigms in psychology and linguistics to better understand the capabilities of models compared to humans. For example, Wilcox et al. (2023) tested text language models using psycholinguistic experimental paradigms, showing that they are capable of learning syntactic dependencies with relatively little input data.

On the speech side, Lavechin et al. (2023) presented evidence that self-supervised SLMs can develop limited language-specific perception. Cruz Blandon et al. (2023) proposed comparing model behavior using checkpoints in the SLM pretraining process with data in child language development. They showed that computational language models can be a valuable resource in testing or confirming linguistic theories in the language development field. The methodology is mostly concerned with the overall learning of the language model in the output stage. Our work contributes to the explanation of the inner workings of SLMs.

### Human perception experiments

In terms of tone perception, F0 is a clear primary cue (Ryant et al., 2014b; Rhee et al., 2021; Chen et al., 2022), but other secondary pitch cues serve to assist when speech is ambiguous and/or disrupted. Given that conversational speech contains nontrivial speech recognition difficulties such as e.g. tone sandhi and coarticulation, individual variation, and context omission (Ryant et al., 2014b), secondary cues play a role in the distinction of tones. An example of this is voice quality, where for example lowering F0 (introducing 'creaky' voice) increased perceptual saliency for T3, whereas T1 and T4 accuracy decreased and T2 remained unaffected (Huang, 2020; Chai, 2019; Kuang, 2017). This emphasises the fact that F0 does not operate in isolation, but that covariation between pitch and voice quality is inherent in Mandarin. Spectral cues (e.g. amplitude differences, spectral tilt) have also been suggested to be sufficient for adult speakers in tone production, while children are thought to hyperatticulate the tonal differences in speech (Rhee et al., 2021).

Suprasegmental cues appear to be preferred in experiments that compare segmental and suprasegmental cues against each other. Human infants are more sensitive to suprasegmental cues, with even newborns showing the same preference (Mehler et al., 1988; Nazzi et al., 1998). Several studies observe that tonal sensitivity develops earlier than perception of vowels and consonants (Xi et al., 2009; Yeung et al., 2013), with sensitivity to non-native tonal distinctions remaining longer than perception of non-tonal non-native phoneme categories (Liu and Kager, 2014; Shi et al., 2017) 

Comparing vowels, consonants and tones, Singh et al. (2015) show that Mandarin learning children's sensitivity to consonants and vowels develop at a similar rate and shows departure from tones. The effect of tone mispronunciation is much larger than that of vowel or consonant mispronunciation for toddlers, but the pattern is reversed in preschoolers Singh et al. (2015).

### Automatic classification of tones

Automatic tone classification in Mandarin traditionally uses F0 contour and mel-frequency cepstral coefficients (MFCC) features. Advances in deep learning brought improvements in performance of tone classification. Ryant et al. (2014a) compare MFCC features and F0 contour as input to a neural tone classifier. MFCC features, while not explicitly encoding the F0 contour information, achieve an error rate of 15.56% for Tone 1-4 classification. The combination of MFCC features and F0 contours extracted with different methods did not see an improvement in the classifier's performance, indicating that the classifier was able to extract F0 contour from the MFCC features, or it was able to predict Mandarin tones reliably without F0 contour information. However, it is possible that the classifier was able to exploit associations between specific phonemes strings and tone labels, and hence avoid learning to detect tone based on pitch and voice quality cues.

After the introduction of self-supervised SLMs, Yuan et al. (2021) fine-tuned an English pre-trained wav2vec2 model Baevski et al. (2020) for Mandarin tone classification and achieved a tone error rate of 6% on the same dataset as Ryant et al. (2014a). Clearly, SLMs can handle the task of classifying Mandarin lexical tone with labeled fine-tuning. The aim of the present paper is not to compete with the existing implementations of Mandarin tone classifiers; rather we aim to uncover the tone encoding capabilities that emerge without explicit supervision.

## 4 Methodology

We use a number of wav2vec2-based Baevski et al. (2020) models pre-trained and fine-tuned on datasets of different languages for our investigation. As examples of tonal languages, we choose Mandarin, Vietnamese and Cantonese, whereas English and French serve as non-tonal language examples. The models trained in the languages above are then tested on test data from Mandarin and Vietnamese.

To examine the encoding of tone, we train linear probing classifiers on the hidden state activations extracted from the aforementioned models for every morpheme in our testing datasets. 2

Footnote 2: We release the codebase for our experiments at [https://github.com/techsword/tone-encoding-in-speech-model](https://github.com/techsword/tone-encoding-in-speech-model)

### Datasets

Training data.We examine SLMs that were trained on datasets of the following languages:

**Mandarin** pre-trained with AISHELL-2 Du et al. (2018) and fine-tuned with AISHELL-1 Bu et al. (2017). **English** pre-trained and fine-tuned with LibriSpeech Panayotov et al. (2015). **Vietnamese** pre-trained with unlabelled YouTube audio and fine-tuned with the VLSP dataset for ASR Nguyen (2021). **Cantoese** pre-trained on a combined dataset of older Cantonese adult speech and YouTube audio Huang and Mak (2023). **French** pre-trained on MLS French Pratap et al. (2020). Table 1 summarizes the characteristics of these datasets.

Test data.We primarily use the Mandarin Chinese THCHS-30 dataset Wang and Zhang (2015) for testing models' encoding of Mandarin tone. THCHS-30 consists of 30 hours of Mandarin speech recorded in a laboratory environment. The dataset is transcribed into both Chinese characters and Mandarin Pinyin. We also obtain character-level forced alignment with the Charsiu aligner Zhu et al. (2022).

To test the generalizability of our results, we also use the Vietnamese VIVOS dataset Luong and Vu (2016), which consists of 15 hours of Vietnamese read speech recorded in a laboratory environment. The dataset is transcribed into Vietnamese orthography. We then convert the transcription into International Phonetic Alphabet (IPA) with tone labels with vPhon Kirby (2008). We use the Montreal Forced Aligner McAuliffe et al. (2017) to obtain a syllable-level forced alignment.

Pre-training data.For the experiments on SLM's learning trajectory and perceptual patterns (see Section 5.3), we pre-train SLMs from scratch on the following datasets:

* MAGICDATA Magic Data Technology Co. (2019), containing 755 hours of read Mandarin Chinese. The dataset was pre-split into a 712-hour training set and a 28-hour validation set.

 

* LibriSpeech Panayotov et al. (2015), see details in Table 1. We split a subset of the LibriSpeech dataset into a 710-hour training set and a 29-hour validation set.

### Spoken Language Models Architecture.

With the exception of the Cantonese model, all models investigated in this paper are based on the base configuration of wav2vec2 Baevski et al. (2020). Wav2vec2-base consists of five convolutional feature encoder and twelve transformer layers. The feature encoder processes the audio waveform input into latent speech representations, and the transformer layers encode the feature encoder output into contexual representations. The wav2vec2-base models has 95M parameters. The Cantonese model uses the wav2vec2-conformer architecture with 180M parameters.

Training objectives.The fully self-supervised _pre-training_ objective in wav2vec2 consists in discriminating between the matched and unmatched segment representations for a masked portion of the latent speech representation. The ASR _fine-tuning_ objective consists in transcribing the audio input into output tokens in the orthography of the target language and is realized by adding a linear layer on top of a pre-trained wav2vec2 model.

Checkpoints.For the experiments in Section 5.3 we pre-train two SLMs with the fairseq toolkit Ott et al. (2019) on LibriSpeech for English and MAGICDATA for Mandarin; we train both models for 85,000 steps using 8 Nvidia A100-40GB GPU with update frequency = 8 to simulate training with 64 GPUs. Each model finished training in approximately 96 hours. We save checkpoints every 5,000 steps.

### Probing classifiers

Preprocessing.We follow previous work Ryant et al. (2014a) in removing segments transcribed with the neutral tone from the Mandarin tone classification task. Mandarin neutral tones primarily appear in unstressed syllables (cf. Section 2) and hence are more susceptible to variations.

Generating classifier input.We extract the hidden state activations of models as a response to audio samples in the test data. We average-pool the hidden state output corresponding to the duration of individual syllables to obtain a vector using forced alignment timestamps. The resulting 768-dimensional vectors are input to the classifiers. To control for the influence of lexical cues on tone detection, we construct an exclusive train-test-split such that phoneme strings appearing in the test set do not appear in the training set. This setup prevents the probing classifier from exploiting associations between tones and phoneme sequences. We employ a randomized 80:20 train-test split with the split sizes shown in Table 2.

F0 and MFCC baselines.We closely follow Ryant et al. (2014a) and use F0 contours and 40-dimensional mel-frequency cepstral coefficients (MFCC) features as baselines. We use Librosa McFee et al. (2023) to extract the MFCC features and Praat Jadoul et al. (2018); Boersma and Weenink (2021) to extract the F0 contours from the audio samples. We then find the center frame for each word using the alignment timestamps and concatenate all frames in a 21 frame window (10

\begin{table}
\begin{tabular}{l l r r r} \hline \hline  & \multicolumn{4}{c}{Size (hours)} \\ Training language & Tonality & Pre-training & Fine-tuning & Speech type \\ \hline English Baevski et al. (2020) & Non-tonal & 960 & 960 & Read \\ French Parcollet et al. (2023) & Non-tonal & 1,000 & - & Read \\ \hline Mandarin Lu and Chen (2022) & Tonal & 1,000 & 178 & Read \\ Vietnamese Nguyen (2021) & Tonal & 13,000 & 250 & YouTube audio/Read \\ Cantonese Huang and Mak (2023) & Tonal & 2,800 & - & Spontaneous + Read \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the datasets used in pre-training/fine-tuning models.

\begin{table}
\begin{tabular}{l c c} \hline \hline Language & Split & Samples \\ \hline Mandarin & Train & 223,851 \\ Mandarin & Test & 45,772 \\ Vietnamese & Train & 124,248 \\ Vietnamese & Test & 29,629 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Train/test splits for the tone probing classifier, for the Mandarin and Vietnamese data.

 

1-10) for both F0 and MFCC features. We end up with a 21-dimensional vector for F0 contours and 840-dimensional vector for MFCC features as our baseline classifier inputs.

Text baseline.In addition to audio baselines, we also include a text-based transformer model in our comparison. BERT Devlin et al. (2019) serves as a reference point to show how much information is encoded in the speech signal as opposed to what can be guessed from pure text. We use a Chinese pre-trained BERT3 that encodes Chinese characters into vectors. We extract per-word hidden state outputs with a resulting 768-dimensional vector.

Footnote 3: [https://huggingface.co/bert-base-chinese](https://huggingface.co/bert-base-chinese)

Tone classifiers.We use the syllable activation vectors as input to a Ridge linear classifier that predicts the lexical tone of the input morpheme. We select the final model via 5-fold cross-validation, and report the classification accuracy on the test split. The regularization strength \(\alpha\) was tuned for values \(\{10^{n}~{}|~{}n\in\{-4,-3,-2,-1,0,1,2\}\}\).

Consonant classifiers.When comparing tone to consonant classification, we employ the same classifier setup for consonant and replicate the perception experiment in Wang and Chen (2020) in Section 5.3.1. Since we only investigate consonants that appear solely in the onset position and the rest of the phonemes are not relevant to our task, we use the same syllable vectors as above instead of obtaining a phoneme vector with using phoneme level alignment. We construct exclusive train-test-splits that contain unique rimes (nucleus + coda) of the syllables. Specific details of the train/test split for this experiment can be found in Table 3.

## 5 Results

In this section, we present a series of experiments for analyzing the encoding of tone in SLMs.

### Tone encoding across languages

Figure 2 shows the tone classification accuracy using the layer-wise representations of all models pre-trained on non-tonal (left) versus tonal (right) languages. We see that all layers of all models perform better than the F0 and MFCC baselines, which themselves outperform the text-based BERT baseline. The classification accuracy for tonal language models is overall higher, and increases in the higher layers of the models. Models trained on non-tonal languages also show substantial encoding of tone; but remarkably, there is a substantial drop in classification accuracy in their final layers while the corresponding decrease is much less pronounced in tonal language models.

We repeat the tone classification experiment for Vietnamese tones. Results in Figure 3 show the Cantonese model performs slightly better than the English model, especially towards the later layers; the Mandarin model, however, patterns similar to the English model. This is could be due to the fact that Mandarin has fewer tonal contrasts than Vietnamese and Cantonese (cf. Section 2), or, more

Figure 3: Classification accuracy of Vietnamese lexical tones with hidden-state activations from models pre-trained on tonal and non-tonal languages.

\begin{table}
\begin{tabular}{l c} \hline \hline Split & Samples \\ \hline Train & 92,413 \\ Test & 15,688 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Train/test split for the consonant probing classifier, for the Mandarin data.

Figure 2: Classification accuracy of Mandarin lexical tones using layer-wise representations from models pre-trained on tonal and non-tonal languages.

 

likely, that Vietnamese uses different acoustic cues such as phonation type and voice quality in tonal perception than f0 contours or height in Mandarin Brunelle (2009).

Studies on human participants show that speakers of other tonal languages can perform better at identifying Mandarin lexical tones compared to non-tonal language speakers So and Best (2010), but other literature suggest that listeners are more sensitive to specific cues in tonal perception that are present in their native languages DiCanio (2012); Schaefer and Darcy (2014). The SLMs we tested show the former pattern for Mandarin tone classification. Regarding Vietnamese tones, the result is more equivocal suggesting that Cantonese tone representations generalize to Vietnamese to some extent, while Mandarin ones do not.

### Impact of ASR fine-tuning

We examine how fine-tuning for ASR impacts the encoding of tone in SLMs. Since tonal information is crucial for correctly transcribing tonal language input, SLMs fine-tuned for tonal languages are expected to perform better at our tone classification task. Figure 4 compares the tone classification accuracy of the English and Mandarin models, pre-trained only (left) versus pre-trained and then fine-tuned (right); Figure 5 shows the corresponding results for English and Vietnamese.

We find that fine-tuning affects the encoding of tone for non-tonal vs tonal language models in opposite ways: classification accuracy benefits from fine-tuning for Mandarin, but is harmed by it for the English model. The same pattern holds for English vs Vietnamese on the Vietnamese tone data in Figure 5.

These results likely reflect the fact that ASR fine-tuning encourages the SLM to increase its specialization in identifying the language-specific information needed to output the written form of the language. Tonal information may not contribute much to this objective in non-tonal languages, and thus fine-tuning would tend to remove it. In tonal-language ASR however, tone information may be crucial to correctly transcribe the input audio, for example, when disambiguating Mandarin syllables that consist of the same segmental phonemes and only differ in tone, in order to output the correct Chinese character.

### Comparison to human perception

In this section we report the results motivated by tone and consonant perception patterns in humans.

#### 5.3.1 Learning trajectory

Children have a higher sensitivity to tone than consonant distinctions early on. For children speaking a non-tonal language, this sensitivity towards tone continues longer than sensitivity towards non-native segmental features, i.e. consonants and vowels Shi et al. (2017); Liu and Kager (2014).

Here we aim to determine the corresponding learning trajectory in SLMs by testing them during pre-training. Figure 6 shows the accuracy of classifying Mandarin consonants and tones in the best performing layer of SLMs trained on English and Mandarin as a function of the number of training steps.

Although we observe classification accuracy of the SLMs quickly surpasses the F0 and MFCC baselines after 10,000 steps, we do not detect an

Figure 4: Classification accuracy of Mandarin lexical tones using layer-wise representations from models pretrained and fine-tuned on Mandarin and English.

Figure 5: Classification accuracy of Vietnamese lexical tones using layer-wise representations from models pre-trained and fine-tuned on Vietnamese and English.

 

obvious difference in the overall pattern between the case of consonants and tones. This suggest that SLMs do not follow the same differential trajectory as children, at least as measured via our methodology.

#### 5.3.2 Tone and consonant contrasts

Non-native speakers can have difficulty distinguishing between T2-T3 and T1-T4 tone pairs in Mandarin Hao (2012). While native adult listeners have shown near perfect tone identification accuracy So and Best (2010); Tsukada and Kondo (2019), there is literature documenting native Mandarin-learning toddlers and also some adults having more difficulty in distinguishing the tone pair T2-3 compared to other combinations of Mandarin lexical tone. Huang and Johnson (2011) showed that the tone pair T2-3 is the most confusable for both native speakers of American English and Mandarin while T1-4 also being confusable for native Mandarin speakers. We investigate this pattern in pre-trained SLMs via a dedicated probing experiment, using the final (85,000 steps) checkpoint of the pretrained models in Section 5.3.1. As can be seen in Figure 7, tone pairs T1-4 and T2-3 show the largest differences in the best classification accuracy between the Mandarin and English models, which roughly matches human perceptual pattern.

We complement the results on the development of tone contrast with a parallel experiment on those Mandarin consonant contrasts which are challenging for speakers of English. Each member of a contrasting group is perceived as the same phoneme by English speakers due to perceptual assimilation Wang and Chen (2020). Table 4 displays the resulting mapping to English phoneme categories.

Figure 8 shows that accuracy for consonant groups 2 and 3 match closely for the two models. Group 1 shows a discrepancy, possibly due to the potential mapping of Mandarin x /c/ into two English consonants sh /l/ and z /z/, as hypothesized by Wang and Chen (2020).

## 6 Conclusion

We analyze the tone encoding capabilities of spoken language models trained on three tonal and two non-tonal languages, using classifier probes with data from two tonal languages: Mandarin and Vietnamese. We find that SLMs trained on either tonal or non-tonal languages encode tonal information in Mandarin and Vietnamese to a significant degree.

We also find that fine-tuning for the speech recognition task enhances the tone encoding capabilities of models trained on tonal languages but reduces them for models trained on non-tonal languages. While we see evidence suggesting that the learning trajectories of SLMs in pre-training do not follow the same developmental trajectories found in human language acquisition, we find that SLMs show patterns similar to that of human listeners in tone and consonant perception experiments.

While this paper focused on investigating the encoding of lexical tones with Mandarin and Vietnamese as case studies, Mandarin and Vietnamese are in no way representative of all the diverse tonal languages that exist in the world. The encoding of other suprasegmental features such as stress patterns and intonation is equally important to study in future work. We hope that explaining self-supervised models of spoken languages provides a unique perspective for us to contribute to a better understanding of how languages work. Given the rise of SLMs trained with multilingual datasets Conneau et al. (2021); Radford et al. (2022), it would be interesting to investigate if the multilingual SLMs encode segmentals and suprasegmentals from different languages more robustly. This paper serves as a starting point in the research of

\begin{table}
\begin{tabular}{r|c c|c c}  & \multicolumn{2}{c}{Mandarin} & \multicolumn{2}{c}{English} \\ \hline Group & Pinyin & IPA & Alphabet & IPA \\
1 & sh, x & s, c & sh & f \\
2 & ch, zh, q & ts\({}^{\text{h}}\), ts, tc\({}^{\text{h}}\) & ch & tf \\
3 & s, z, c & s, fs, ts\({}^{\text{h}}\) & s & s \\ \hline \end{tabular}
\end{table}
Table 4: Perceptual mapping of Mandarin consonants onto English consonants Wang and Chen (2020).

Figure 6: Classification accuracy of Mandarin lexical tones versus consonants for models pre-trained on English and Mandarin.

 

the encoding of suprasegmental cues in spoken language models.

## 7 Limitations

We selected SLMs based on the wav2vec2 architecture in our experimental design, but we acknowledge that the training data of the models selected is quite varied in their size and quality (noisy vs clean speech) as described in Section 4.1. This is partially due to the scarce availability of (high-quality) speech data for underrepresented languages, especially the many tonal languages of the world. Hence SLMs pre-trained on monolingual datasets of these languages are also sparse. The Vietnamese wav2vec2 model Nguyen et al. (2021) was trained on a significantly larger amount of data (13k hours) than the other models tested (around 1000 hours). It is possible that in addition to the inclusion of tonal languages in training, the amount of training data also played a role in increasing the tonal encoding capabilities of SLMs. However, literature has shown that more training data does not always have a positive impact on the models performance if the additional data is noisy Parcollet et al. (2023). At the same time, we note that the Cantonese model Huang and Mak (2023), in addition to being pre-trained on a larger dataset, is also different in architecture. Additionally, our use of two read speech datasets as test data does not fully reflect the linguistic diversity of different accents and dialects in Mandarin and Vietnamese. Future work needs to go wider and deeper in both model architecture and dataset diversity in order to uncover more generalizable patterns in different languages.

## 8 Acknowledgements

This publication is part of the project _InDeep: Interpreting Deep Learning Models for Text and Sound_ (with project number NWA.1292.19.399) of the National Research Agenda (NWA-ORC) program. Funding by the Dutch Research Council (NWO) is gratefully acknowledged. We also thank SURF (www.surf.nl) for the support in using the National Supercomputer Snellius.

## References

* Abdullah et al. (2021) Badr M. Abdullah, Iuliia Zaitova, Tania Avgustinova, Bernd Mobius, and Dietrich Klakow. 2021. How Familiar Does That Sound? Cross-Lingual Representational Similarity Analysis of Acoustic Word Embeddings.
* Baevski et al. (2020) Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, and Michael Auli. 2020. Wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations.
* Breiman et al. (2016)

Figure 8: Classification accuracy for Mandarin consonant groups, for English and Mandarin models. The F0 baseline with its much lower classification accuracy is omitted from this figure for clarity.

Figure 7: Binary classification accuracy for Mandarin tonal pairs, for English and Mandarin models.

 

Martijn Bartelds, Wieste de Vries, Faraz Sanal, Caitlin Richter, Mark Liberman, and Martijn Wieling. 2022. Neural representations for modeling variation in speech. _Journal of Phonetics_, 92:101137.
* Belotel-Grenie and Grenie (1994) Agnes Belotel-Grenie and Michel Grenie. 1994. Phonation types analysis in standard chinese. In _3rd International Conference on Spoken Language Processing, ICSLP 1994_, pages 343-346. The International Society for Computers and Their Applications (ISCA).
* Boersma and Weenink (2021) Paul Boersma and David Weenink. 2021. Praat: Doing phonetics by computer [Computer program].
* Brunelle (2009) Marc Brunelle. 2009. Tone perception in northern and southern vietnamese. _Journal of Phonetics_, 37(1):79-96.
* Bu et al. (2017) Hui Bu, Jiayu Du, Xingyu Na, Bengu Wu, and Hao Zheng. 2017. AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline.
* Chai (2019) Yuan Chai. 2019. The source of creak in Mandarin utterances. In _Proceedings of ICPhS 2019_.
* Chen (2000) Matthew Y. Chen. 2000. _Tone Sandhi: Patterns across Chinese Dialects_. Cambridge Studies in Linguistics. Cambridge University Press, Cambridge.
* Chen et al. (2022) Yue Chen, Yingming Gao, and Yi Xu. 2022. Computational Modelling of Tone Perception Based on Direct Processing of f0 Contours. _Brain Sciences_, 12(3):337.
* Conneau et al. (2021) Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, and Michael Auli. 2021. Unsupervised Cross-Lingual Representation Learning for Speech Recognition. In _Interspeech 2021_, pages 2426-2430. ISCA.
* Conneau et al. (2018) Alexis Conneau, German Kruszewski, Guillaume Lample, Loic Barrault, and Marco Baroni. 2018. What you can cram into a single S&H#' vector: Probing sentence embeddings for linguistic properties. In _Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)_, pages 2126-2136, Melbourne, Australia. Association for Computational Linguistics.
* Hamden et al. (2023) Maria Andrea Cruz Blandon, Alejandrina Cristia, and Okko Rasanen. 2023. Introducing Meta-analysis in the Evaluation of Computational Models of Infant Language Development. _Cognitive Science_, 47(7):e13307.
* de Seyssel et al. (2022) Maureen de Seyssel, Marvin Lavechin, Yossi Adi, Emmanuel Dupoux, and Guillaume Wisniewski. 2022. Probing phoneme, language and speaker information in unsupervised speech representations. In _Interspeech_.
* Devlin et al. (2019) Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.
* DiCanio (2012) Christian T. DiCanio. 2012. Cross-linguistic perception of funyoso Trique tone. _Journal of Phonetics_, 40(5):672-688.
* Du et al. (2018) Jiayu Du, Xingyu Na, Xuechen Liu, and Hui Bu. 2018. AISHELL-2: Transforming Mandarin ASR Research Into Industrial Scale.
* Hao (2012) Yen-Chen Hao. 2012. Second language acquisition of Mandarin Chinese tones by tonal and non-tonal language speakers. _Journal of Phonetics_, 40(2):269-279.
* Hewitt and Manning (2019) John Hewitt and Christopher D. Manning. 2019. A Structural Probe for Finding Syntax in Word Representations. In _Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)_, pages 4129-4138, Minneapolis, Minnesota. Association for Computational Linguistics.
* Hsu et al. (2021) Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kwshal Lakhotia, Ruslan Salakhutdinov, and Abdelrahman Mohamed. 2021. HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units.
* Huang and Mak (2023) Renzo Huang and Brian Mak. 2023. wav2vec 2.0 ASR for Cantonese-Speaking Older Adults in a Clinical Setting. In _INTERSPEECH 2023_, pages 4958-4962. ISCA.
* Huang and Johnson (2011) Tsan Huang and Keith Johnson. 2011. Language Specificity in Speech Perception: Perception of Mandarin Tones by Native and Nonnative Listeners. _Phonetrica_, 67(4):243-267.
* Huang (2020) Yaqian Huang. 2020. Different attributes of creaky voice distinctly affect Mandarin tonal perception. _The Journal of the Acoustical Society of America_, 147(3):1441-1458.
* Hyman (2018) Larry M. Hyman. 2018. What tone teaches us about language. _Language_, 94(3):698-709.
* Jadoul et al. (2018) Yannick Jadoul, Bill Thompson, and Bart de Boer. 2018. Introducing Parselmouth: A Python interface to Praat. _Journal of Phonetics_, 71:1-15.
* Jun and Kubozono (2020) Sun-Anh Jun and Haruo Kubozono. 2020. Asian Pacific Rim. In Carlos Gussenhoven and Aoju Chen, editors, _The Oxford Handbook of Language Prosody_, Oxford Handbooks. Oxford University Press, Oxford, New York.
* Kirby (2008) James Kirby. 2008. vPhon: A Vietnamese phoneticer (version 2.1.1).
* Kirby (2011) James P. Kirby. 2011. Vietnamese (hanoi wietnamese). _Journal of the international phonetic association_, 41(3):381-392.
* Kuang (2017) Jianjing Kuang. 2017. Covariation between voice quality and pitch: Revisiting the case of Mandarin creaky voice. _The Journal of the Acoustical Society of America_, 142(3):1693-1706 

Marrin Lavechin, Maureen De Seyssel, Mariarine Metais, Florian Metze, Abdelrahman Mohamed, Herve Bredin, Emmanuel Dupoux, and Alejandrina Cristia. 2023. Statistical learning models of early phonetic acquisition struggle with child-centered audio data.
* Liu and Kager (2014) Liquan Liu and Rene Kager. 2014. Perception of tones by infants learning a non-tone language. _Cognition_, 133(2):385-394.
* Lu and Chen (2022) Ke-Han Lu and Kuan-Yu Chen. 2022. A context-aware knowledge transferring strategy for CTC-based ASR.
* Luong and Vu (2016) Hieu-Thi Luong and Hai-Quan Vu. 2016. A non-expert Kaldi recipe for Vietnamese speech recognition system. In _Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies (WLSION/OIAF4HLT2016)_, pages 51-55, Osaka, Japan. The COLING 2016 Organizing Committee.
* 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)_, pages 311-315.
* Ltd (2019) Ltd. Magic Data Technology Co. 2019. MAGICDATA Mandarin Chinese Read Speech Corpus.
* Martin et al. (2023) Kinan Martin, Jon Gauthier, Canan Breiss, and Roger Levy. 2023. Probing Self-supervised Speech Models for Phonetic and Phonemic Information: A Case Study in Aspiration. In _INTERSPEECH 2023_, pages 251-255. ISCA.
* McAuliffe et al. (2017) Michael McAuliffe, Michaela Socolof, Sarah Mihuc, Michael Wagner, and Morgan Sonderegger. 2017. Montreal Forced Aligner: Trainable Text-Speech Alignment Using Kaldi. In _Interspeech 2017_, pages 498-502. ISCA.
* McFee et al. (2023) Brian McFee, Matt McVicar, Daniel Faronbi, Iran Roman, Matan Gover, Stefan Balke, Scott Seyfarth, Agoub Malek, Colin Raffel, Vincent Lostanen, Benjamin Van Nieikirk, Dana Lee, Frank Cwitkowitz, Frank Zalkow, Oriol Nieto, Dan Ellis, Jack Mason, Kyungyun Lee, Bea Steers, Emily Halvachs, Carl Thome, Fabian Robert-Stioter, Rachel Buttner, Ziyao Wei, Adam Weiss, Eric Battenberg, Keumwo Choi, Ryuichi Yamamoto, Ced Carr, Alex Metsai, Stefan Sullivan, Pius Friesch, Asmitha Krishnakumar, Shunsuke Hidaka, Steve Kowalik, Fabian Keller, Dan Mazur, Alexandre Chabot-Leclerc, Curtis Hawthorne, Chandrashekhar Ramaprasad, Myungcholu Keum, Juanita Gomez, Will Monroe, Viktor Andrevitch Morozov, Kai Eliasi, Null mightbyfoo, Paul Biberstein, N. Dorukhan Sergin, Romain Hennequin, Rimvydas Nakttinis, Beant towel, Taewoon Kim, Jon Peter Asen, Joon Lim, Alex Malins, Dario Herenu, Stef Van Der Strujjk, Lorenz Nickel, Jackie Wu, Zhen Wang, Tim Gates, Matt Vollrath, Andy Sarroff, Xiao-Ming, Alastair Porter, Seth Kranzler, Voodahoup, Mattia Di Gangi, Helmi Jinzo, Connor Guerrero, Abdutyayb Mazrud, Toddme2178, Zvi Baratz, Anton Kostin, Xinlu Zhuang, Cash TingHim Lo, Pavel Campr, Eric Semeniuc, Monsij Biswal, Shayenne Moura, Paul Brossier, Hojin Lee, and Waldir Pimenta. 2023. Librosa- 0.10.1. Zenodo.
* Mehler et al. (1988) J. Mehler, P. Jusczyk, G. Lambertz, N. Halsted, J. Bertoncini, and C. Amiel-Tison. 1988. A precursor of language acquisition in young infants. _Cognition_, 29(2):143-178.
* Nazzi et al. (1998) T. Nazzi, J. Bertoncini, and J. Mehler. 1998. Language discrimination by newborns: Toward an understanding of the role of rhythm. _Journal of Experimental Psychology. Human Perception and Performance_, 24(3):756-766.
* Nguyen (2021) Thai Binh Nguyen. 2021. Vietnamese end-to-end speech recognition using wav2vec 2.0.
* Ott et al. (2019) Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019. Fairseq: A Fast, Extensible Toolkit for Sequence Modeling. pages 48-53, Minneapolis, Minnesota. Association for Computational Linguistics.
* Panayotov et al. (2015) Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur. 2015. Librispeech: An ASR corpus based on public domain audio books. In _2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)_, pages 5206-5210.
* Percollet et al. (2023) Titouan Percollet, Ha Nguyen, Solene Evian, Marcely Zanon Boito, Adrien Pupier, Salima Mdhaffar, Hang Le, Sina Alisamir, Natalia Tomashenko, Marco Dinarelli, Shucong Zhang, Alexandre Allauzen, Maximin Coavoux, Yannick Esteve, Mickael Rouvier, Jerome Goulain, Benjamin Lecouteux, Francois Portet, Solange Rossalato, Fabien Ringeval, Didier Schwab, and Laurent Besacier. 2023. LeBenchmark 2.0: A Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech.
* Pasad et al. (2024) Ankita Pasad, Chung-Ming Chien, Shane Settle, and Karen Livescu. 2024. What Do Self-Supervised Speech Models Know About Words?
* Pasad et al. (2021) Ankita Pasad, Ju-Chieh Chou, and Karen Livescu. 2021. Layer-wise Analysis of a Self-supervised Speech Representation Model.
* Pratap et al. (2020) Vineel Pratap, Qiantong Xu, Anuroop Sriram, Gabriel Synnaeve, and Ronan Collobert. 2020. MLS: A Large-Scale Multilingual Dataset for Speech Research. In _Interspeech 2020_, pages 2757-2761.
* Radford et al. (2022) Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, and Ilya Sutskever. 2022. Robust Speech Recognition via Large-Scale Weak Supervision 

Nari Rhee, Aoju Chen, and Jianjing Kuang. 2021. Going beyond F0: The acquisition of Mandarin tones. _Journal of Child Language_, 48(2):387-398.
* Ryant et al. (2014a) Neville Ryant, Malcolm Slaney, Mark Liberman, Elizabeth Shriberg, and Jiahong Yuan. 2014a. Highly Accurate Mandarin Tone Classification In The Absence of Pitch Information. In _Speech Prosody 2014_, pages 673-677. ISCA.
* Ryant et al. (2014b) Neville Ryant, Jiahong Yuan, and Mark Liberman. 2014b. Mandarin tone classification without pitch tracking. In _2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)_, pages 4868-4872, Florence, Italy. IEEE.
* Schaefer and Darcy (2014) Vance Schaefer and Isabelle Darcy. 2014. Lexical function of pitch in the first language shapes crosslinguistic perception of Thai tones. _Laboratory Phonology_, 5(4):489-522.
* Shen et al. (2023) Gaofei Shen, Afra Alishahi, Arianna Bisazza, and Grzegorz Chrupala. 2023. Wave to Syntax: Probing spoken language models for syntax. In _INTERSPEECH 2023_, pages 1259-1263.
* Shi et al. (2017) Rushen Shi, Jun Gao, Andre Achim, and Aijun Li. 2017. Perception and Representation of Lexical Tones in Native Mandarin-Learning Infants and Todders. _Frontiers in Psychology_, 8.
* Singh and Fu (2016) Leher Singh and Charlene S. L. Fu. 2016. A New View of Language Development: The Acquisition of Lexical Tone. _Child Development_, 87(3):834-854.
* Singh et al. (2015) Leher Singh, Hwee Hwee Goh, and Thilanga D. Welawalarachchi. 2015. Spoken word recognition in early childhood: Comparative effects of vowel, consonant and lexical tone variation. _Cognition_, 142:1-11.
* So and Best (2010) Connie K. So and Catherine T. Best. 2010. Crosslanguage Perception of Non-native Total Contrasts: Effects of Native Phonological and Phonetic Influences. _Language and speech_, 53(Pt 2):273-293.
* Tsukada and Kondo (2019) Kimiko Tsukada and Mariko Kondo. 2019. The Perception of Mandarin Lexical Tones by Native Speakers of Burmese. _Language and Speech_, 62(4):625-640.
* Vaswani et al. (2017) Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention Is All You Need.
* Wang and Zhang (2015) Dong Wang and Xuewei Zhang. 2015. THCHS-30 : A Free Chinese Speech Corpus.
* Wang and Chen (2020) Xinchun Wang and Jidong Chen. 2020. The Acquisition of Mandarin Consonants by English Learners: The Relationship between Perception and Production. _Languages_, 5(2):20.
* Wilcox et al. (2023) Ethan Gottlieb Wilcox, Richard Futrell, and Roger Levy. 2023. Using Computational Models to Test Syntactic Learnability. _Linguistic Inquiry_, pages 1-44.
* Wu et al. (2020) Yaru Wu, Martine Adda-Decker, and Lori Lamel. 2020. Mandarin lexical tones: A corpus-based study of word length, syllable position and prosodic position on duration. pages 1908-1912.
* Xi et al. (2009) Jie Xi, Wei JIANG, Linjun Zhang, and Hua Shu. 2009. Categorical Perception of VOT and Lexical Tones in Chinese and the Developmental Course. _Acta Psychologica Sinica_, 41:572-579.
* Yeung et al. (2013) H. Henny Yeung, Ke Heng Chen, and Janet F. Werker. 2013. When does native language input affect phonetic perception? The precocious case of lexical tone. _Journal of memory and language_, 68(2):123-139.
* Yip (2002) Moira Yip. 2002. _Tone_. Cambridge Textbooks in Linguistics. Cambridge University Press, Cambridge.
* Yuan et al. (2021) Jiahong Yuan, Neville Ryant, Xingyu Cai, Kenneth Church, and Mark Liberman. 2021. Automatic recognition of suprasegmentals in speech.
* Zee (1991) Eric Zee. 1991. Chinese (Hong Kong Cantonese). _Journal of the International Phonetic Association_, 21(1):46-48.
* Zhu et al. (2022) Jian Zhu, Cong Zhang, and David Jurgens. 2022. Phone-to-audio alignment without text: A Semi-supervised Approach. _IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)_.