Homophone Disambiguation Reveals Patterns of Context Mixing in Speech Transformers
Authors: Hosein Mohebbi
Grzegorz Chrupała
Willem Zuidema
Afra Alishahi
Venue: EMNLP 2023 Main (2023)
Keywords: speech transformers
context mixing
model interpretability for spoken language

Abstract: Transformers have become a key architecture in speech processing, but our understanding of how they build up representations of acoustic and linguistic structure is limited.  In this study, we address this gap by investigating how measures of 'context-mixing' developed for text models can be adapted and applied to models of spoken language.  We identify a linguistic phenomenon that is ideal for such a case study: homophony in French (e.g. livre vs livres), where a speech recognition model has to attend to syntactic cues such as determiners and pronouns in order to disambiguate spoken words with identical pronunciations and transcribe them while respecting grammatical agreement. We perform a series of controlled experiments and probing analyses on Transformer-based speech models. Our findings reveal that representations in encoder-only models effectively incorporate these cues to identify the correct transcription, whereas encoders in encoder-decoder models mainly relegate the task of capturing contextual dependencies to decoder modules.

Homophone Disambiguation Reveals Patterns of Context Mixing in Speech Transformers
Hosein Mohebbi'
Grzegorz Chrupata' Willem Zuidema² Afra Alishahi'
CSAI, Tilburg University ILLC, University of Amsterdam
mohebbh a. jallishhhijttilluuuuniiii grzegorz@chrupalaa.m h. whidzmamuuuuaa. n1
ty..tte.eee edu
Abstract
to transformers trained on text data. In the cur- rent study we investigate context mixing in models trained on spoken language data.
Transformers have become a key architecture speech processing. our understanding of how they build up representations of acoustic and linguistic structure is limited. In this study, we address this gap by investigating how mea- sures of "context-mixing" developed for text models can be adapted and applied to models of spoken language. We identify a linguistic phe- nomenon that is ideal for such case study: ho- mophony in French (e.g. livre vs livres), where speech recognition model has to attend to syn- tactic cues such as determiners and pronouns order to disambiguate spoken words with identical pronunciations and transcribe them while respecting grammatical agreement. We perform series of controlled experiments and probing analyses Transformer-bssed speech models. Our findings reveal that representa- tions in encoder-only models effectively incor- porate these cues to identify the correct tran- scription, whereas encoders in encoder-decoder models mainly relegate the task of capturing contextual dependencies to decoder modules."
We focus on the task of automatic speech recog- nition (ASR), which consists of transcribing spoken utterances into their written equivalent. In order to probe context mixing for speech models, ploit a quirk of the French spelling system: modern spoken French does not general? mark grammat- ical number on nouns, adjectives or third-person verbs, only on determiners and pronouns how- ever written French reflects an earlier period of the language and does mark nouns, adjectives and verbs for number. Therefore, for an ASR system to produce grammatical transcriptions which feature number agreement, the model needs rely very specific cues in the input and/or in the prefix of the generated output.
For example, when transcribing the utterance Elle perdu les livres, meaning "She lost the books", when generating the bolded TARGET word, the model needs decide whether to output livre (singular) rather livres (plural) and it cannot the speech signal corresponding to the target word to choose the correct form, as spoken French both are pronounced /livs/. The correct choice is determined by grammatical agreement with the underlined CUE word, here the plural determiner, which is different from the singular form, both in speech (/le/ vs /la/) and in writing (les vs le).
1 Introduction
In both speech and text processing, variants of the Transformer architecture (Vaswani al., 2017) have become ubiquitous. The key advantage of this neural network topology lies the modeling of pairwise relations between elements of the in- put (tokens): the representation of token at particular Transformer layer is a function of the weighted sum of the transformed representations of all the tokens in the previous layer. This feature of Transformers is known as context mixing and understanding how it functions specific model layers is crucial for tracing the overall information flow. Several works (c.g. Kobayashi et al., 2020, 2021; Mohebbi et al., 2023) have proposed meth- ods to quantify context mixing and applied them
Thanks to this phenomenon we can form a strongly supported prior expectation that the rep- resentation of the cue token should contribute to the representations of the target. We are there- fore able to validate previously suggested context mixing scoring methods on dataset of French ut- terances featuring such grammatical homonyms: find that context-mixing scoring methods, and especially Value Zeroing, behave plausibly, largely
'Code is freely available at https://githun com/ Tmmbabbai/Contexttiinnggg
TThere is a small number of irregular exceptions to this phenomenon.

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agreeing with prior expectations, as well as with targeted diagnostic probes and faithfulness tests. We furthermore apply context-mixing scoring methods to two types of architectures: encoder- decoder and encoder-only, and discover interest- ing differences in how they behave. In encoder- only models, spoken cue-word representations con- tribute target representations in a straightforward manner. Meanwhile, in mecoerrddecodr architec- tures is mostly the decoder which ensures gram- matical number agreement, and therefore spoken cue-word representations contribute little to targets, while written cue-words in the generated prefix make decisive contribution.
measure the diversity of attention heads in Con- former and find that the attention probabilities from different heads show low diversity (analogous to the redundant pattern of attention pre-trained language models) during the course of training, im- plying that they mostly focus on the same frames. In contrast, the value vectors show high diversity among different heads.
2.2 Analyses of context mixing
While self-attention weights were initially deemed as the primary souree of information mixing in Transformers (Clark al., 2019; Kovaleva et al., 2019; Reif al., 2019; Lin et al., 2019), recent studies have shown that integrating other compo- nents in the architecture can yield more reliable measures (Kobayashi et al., 2020, 2021; Modar- ressi ct al., 2022; Ferrando et al., 2022; Mohebbi et al., 2023). Kobayashi et al. (2020) proposed a context-mixing technique called Attention Norm which is based the norm of the transformed value vectors, and demonstrated that the model may assign a high attention weight token with a small norm, particularly for highly frequent and less informative words. More recently, Mohebbi et al. (2023) introduced a technique called Value Zeroing, which offers more reliable context mixing predictions compared to alternatives, mainly due to incorporating the output representations of Trans- former layers that encompass all the components within the layer, including residual connections and networks.
2
Related Work
Our work builds on two strands of research: one focusing on analyzing models of spoken language. and the other focusing specifically on quantifying information flow due to context-mixing in Trans- former models of textual data.
2.1 Analyses of speech models
A large body of work on analyzing Transformer- based speech models has focused on probing their representations to uncover various types of linguis- tic structures they are hypothesized to encode, such as phonemic, lexical syntactic information, as well as non- non-linguistic information such as speaker iden- tity and demographics (Pasad et al., 2021; Shah al., 2021; Millet and Dunbar, 2022; Yang et al., 2023; Shen al., 2023). Pasad et al. (2021) showed that in pre-trained wav2vee 2.0 models (Baevski et al., 2020), the representations evolve layer by layer according to an acoustic- longuuic-i hicrarchy: the shallowest layers primarily encode acoustic fea- tures, followed by layers encoding phonetic, lexi- cal, and semantic information. This trend then re- verses in the deeper layers, unless the model is fine- tuned for ASR. More recently, Shen et al. (2023) showed that pre-trained wav2vec 2.0 models cap- ture some degree of syntactic structure, particularly in the middle layers of the network.
These techniques are developed for and evalu- ated on text. In this study we borrow the of the-art context mixing methods, Attention Norms and Value Zeroing, and extend their applicability to both encoder-only and encoder-deccderr-bel speech models.
3 Context Mixing
Our goal is to quantify the contribution of cue word to the representation of target word across layers of speech Transformers. Here we first review the formation of contextualized representations in these models, and then describe how we adapt text- based context mixing methods for our purpose.
Another strand of research is focused on ana- lyzing self-attention weights inside Transformer layer simple measure of pairwise interaction of frame representations. By analyzing self-attention weights in Conformer (Gulati al., 2020), Shim et al. (2022) observe two distinct patterns for pho- netic and linguistic localization in the lower and upper layers, respectively. Audhkhasi et al. (2022)
3.1 Background
Transformer Encoder. A speech encoder takes as input a sequence of audio representations known as audio tokens, or frames, and passes them through

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stack of encoders to make contextualized audio representations. These tokens are typically the output of convolutional feature extractor mod- ule applied to the acoustic waveform. Given a sequence of d-dimensional input audio represen- tations (I1, ..... .T). the goal of Transformer en- coder layer is to build contextualized audio repre- sentations (i1,..., k for each frame in the con- text. This process consists of two sublayers: multi-head self-attention mechanism (MHA) and a position- -iite fully connected ferw-fowward net- work (FFN).
Transformer Decoder. In encoder-decoder- based ASR models, the model receives textual tokens as input to generate a probability distri- bution over the target vocabulary. The decoder then constructs contextualized representations based on both the previously generated text tokens from earlier time steps and the final encoded audio representation. The procedure is similar to encoders, except they perform the MHA procedure (Eq. 5) twice in two sublayers. The first MHA sublayer is modified to prevent tokens attending to subsequent positions when forming context vectors. The second MHA sublayer uses the key and value vectors from the last encoder layer to incorporate encoded audio representations into computation through cross-attention.
After passing through a layer normalization (LN)', each imput vector in MHA module is com- pressed into separate query (q key (k."), and value (ef²) vectors via trainable linear transforma- tions for each head h E {1,..., H):
Decoder-less ASR. For the encoder-only archi- tecture, ASR carried by outputting transerip- tion tokens directly via a connectionist temporal classification (CTC) layer on top of the encoder output.
d LNMHA (z:) W bb
K LNMHA(Z;)) W +b*
(2)
of LNMIIM W + +b
3.2 Analysis methods
In an encoder-decoder model, we compute three types of context mixing scores: within-encoder, within-decoder and cross (between encoder and decoder).
The raw attention weight aij representing how much the 3hh frame attends the j"h frame within the context, is then computed by scaled dot-product between the corresponding query and key vectors followed by the softmax function:
Within-encoder scores measure the contribution of audio frame representations to a target frame representation in the encoder.
qiki
aijj softmax
R
(4)
V
Within-decoder scores measure the contribution of token representations from previous time steps to target token in the decoder.
Consequently, the context vector output from MHA can be formulated weighted sum over the trans- formed value vectors within all frames across at- tention heads:
Cross scores measure the contribution of audio frame representations from the encoder to a target token representation in the decoder.
H 2;
1 a Wh 60 x
(5)
To compute within-eneoder and cross scores for au- dio input, we need to extract frames that correspond to each cue or target word (based on the alignment between the audio and the transcription). Consider sets I and J, representing the frame indices for words i and j, respectively. These sets allow us to aggregate lerd-level context mixing scores (Sx ;) to measure how much the jth word in the context contributes to the representation of the 5th word. In the decoder, these sets contain all the subword tokens corresponding the word of interest, and thus often contain just a single value.
Finally, two linear transformations with a GELU activation function (Hendrycks and Gimpel, 2016) in between are applied to every to produce out- put frame representation ã.::
E. GELU (NHFN(z)) W2 W2+b2+2
(6)
As apposed to the original Transformer architecture (Vaswani ot al., 2017), models studied this paper utilize Pre-LN rather than post-LNN.
We describe each context mixing method below.

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Attention (Attn). Raw attention weights are de- rived from Eq. 4, averaged over all frames for each of the two words, over all heads:
subject noun phrase and verb. Table I shows exam- ples of utterances captured using these templates.
We used the test set the Common Voice (Ardila et al., 2020) spoken corpus*, which consists of 26 hours of transcribed speech in French, and generated dependency trees of transcriptions with SpaCy (Honnibal and Montani, 2017) discover instances of these templates. We selected those amples for which all models in our analysis predict the cue and target words correctly. This resulted in the extraction of 1000 indoo-transciiption pairs." 5
H Ee
ΣEΣ
Si-j I MEIMEJT h=1
n.m
Attention Norm (AN). Motivated by Eq. 5, this metric measures the Euclidean norm of the weighted transformed vector to measure each token's contribution in self-attention module (Kobayashi et al., 2020):
4.2 Target models
H ΣΣ> nCI MEJ h=1
Our experiments cover five prominent off-the-shelf models based on Transformers for learning speech representations, which can be classified into two categories in terms of their architecture and training objectives. See Table 2 for an overview.
Si-j I
la mm w
(8)
Value Zeroing (VZ). It measures how much the output representation of token is affected when excluding the 5"h token by zeroing its value vec- tor (Mohebbi et al., 2023). In the speech context, we set the value vectors in all frames in J (corre- sponding to word j) zero, extract the alternative representations for each frame n E I (that is, 2,7),, and measure how much each frame representation has changed compared to the original ones:
Wh Whisper family, Whisper (Radford al., 2022) multilingual encoder-decoder multitask speech model. takes raw audio as input, splits it into 30- second chunks, and transforms them into log-Mel spectrograms. After passing through two convolu- tion layers, these audio features are then fed into a stack of Transformer encoder layers to create con- textualized audio representations. The model is toregressively trained predict next token on a set of supervised audio-to-text tasks such as language identification, phrase-level timestamps, multilin- gual speech transcription, and speech translation to English. For our experiments, we used Whi Whisper in three different sizes (base, small, medium).
1
Si+i
Σ NEI
cos ( ),n")
4 Experimental Setup
4.1 Data
As case study for analyzing word-level context mixing in audio representations, we opt for ho- mophony in French, where certain grammatical forms of a word are identical in pronunciation, de- spite having different spellings. The spoken forms are thus disambiguated in writing by relying on grammatical agreement phenomena, where nouns agree in number with determiners, and verbs agree with subject pronouns. The nature of this task makes suitable for analyzing context mixing in speech models since it offers a strong hypothesis regarding which part of the context is most relevant when transcribing ambiguous target words.
Wav2vec2 family. This family models em- ploys only the encoder part of the Transformer architecture, built on the wav2vec 2.0 framework (Baevski et al., 2020). The model receives raw au- dio waveform input and maps it into latent speech representations via multi-layer convolutional neu- ral network. These latent representations are par- tially masked (Jiang al., 2019; Wang et al.. 2020) and passed through a stack of Transformer encoder layers to build contextualized representations. The model is then trained by solving a contrastive task over quantized speech representations in self- supervised manner. After pre-training, the model can be fine-tuned on labeled data using Connec-
We define three specific syntactic templates in which homophony may appear. All rely on gram- matical number agreement: the first one captures determiner-noun number agreement; the second number agreement between subject pronoun and verb; and the third - number agreement between
"We only used the test split of corpus since training validation sets have already been utilized during the fine- tuning phase of models within the Wav2vec2 family. 'The details of the data extraction procedure is described in Appendix A.

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Pattern
Examples transcription
#
C'est le septième titre de champion de Syrie de l'histoire du club y mène une vie d' d'études de recherches
Dou_
720
Chaque jour, leurs concurrents les voient sortir de pistes dont ils ignorent I' l'existence On trouve une plage naturiste
Pronoun Verb
257
Pou après cette climination, le club et Alexander se séparent l'amiablc À fin, les enfants se révoltent et détruisent I' l'école.
Dou_Noun_ Verb
Table I: Examples of the extracted audios from the Common Voice corpus based on defined patterns. Last column shows the number of examples obtained. Cue and Target words underlined and bolded, respectively.
Model
Architecture
Size
Layers
Width
Pre-training
Multilingual Fine-tuned
base small medium
512
Whisper
encoder-decoder
12
768
supervised
yes
no
24
1024
XLSR-53
encoder
large
24
1024
self-supervised
yes
yes
XLSR-1
yes
Table 2: Specification of the target models our study.
tionist Temporal Classification (CTC) loss (Graves al., 2006) for downstream speech recognition. For experiments, we used XLSR-536 (Conneau et al., 2020), and XLSSR-7 (Evain et al., 2021) from this family. The former is pre-trained on 56K hours of speech data in 53 languages, whereas the latter is pre-trained on 7K hours of French speech only. Both models are fine-tuned for French ASR using CTC.
to the selected word. In our dataset, average word corresponds 369ms of audio duration and 19 encoder frames.
Experiments
We design a number of experiments meant first validate the context-mixing scoring methods in the context of speech transformers, and further to pro- vide converging evidence on the specific patterns of context-mixing in the two target architectures on our dataset.
4.3 Frame-word alignment
To investigate context mixing in speech models at the word level, need to know which frames in the encoder representation correspond which words in the audio. As the Common Voice dataset does not provide this information, we used Mon- treal Forced Aligner (McAuliffe et al., 2017, MFA) to extract the start (l.)) and end time (t.) of each word in an utterance, and mapped them to boundary frames Ss and fe:
5.1 Cue contribution
To quantify the contribution of the cue word the representation a target word, we compute context mixing maps based on different methods described in Sec. 3.2. For each method, we nor- malize the scores for all words in a sentence so that they are positive values and sum up to one. We then select the tt row of each context mixing map, where t represents the time step at which target word is being generated. The resulting 1-D array indicates how much the target word relies on other words in the context when forming its contextualized representation.
f= - x
(10)
where T and T' denote the total time of given utterance and the total number of frames in the encoded audio, respectively." 8 The range (fs, fe) indicates which frames in the encoder correspond
Recall that all models in our experiments accu- rately transcribe both cue and target words,
"https::/huggiinggacc co/ jonajoaatttassosnn wavzveee-largexxlsr--ffffee
'https: hhttps://huggingffce http::/hhgggnnnnaac.cooooeeeeeer cw/.eeeechharkk/////// wav2vec2-FR-7K-
instead pads the audio input with silence to a constant duration of 30 seconds its processor. Therefore the variables T and T in Eq. 10 are always set as 30 seconds and 1500, respoctively for Whisper models.
"In contrast to the models the Wave2Vee 2.0 family, Whisper does not support attention mask in its encoder and

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specifically extracted the dataset under this condi- tion. As cue words are the sole indicators in the context for identifying the correct transcription of target words, we expect that accurate disambigua- tion of the target word is due to the model's 's reliance on the cue words when constructing target word representations. To measure this reliance, we define binary cue vector C/ according to the following condition:
ercoter
#roste--trabed Irahed
encoddr ranccom
25-
20-
17
Score
14-
23
12-
13
11, the 7th position c Cue word C - 00. otherwise
9-
(11)
6-
We then compute the Cue contribution score as the dot product of the vector (C) and given context mixing score (St).
3.
Results. Figure 1 shows cue contribution scores for all layers of both randomly initialized and trained versions of XLSR-53, which is an encoder- based model. For the model with random weights, the cue contribution scores are low and uniform across the layers. For the trained model, cue con- tribution scores indicate that cues are prominently integrated into the target audio representations in the middle layers (notably, layers 10, 12 and 15).
Figure I: Layer-wise cue contribution according to dif- ferent analysis methods averaged over all examples for XLSR-53, trained (left) vs. randomly initialized (right).
encoder- Entined
CTOWN trained
Secocder trained
This pattern conforms the prior expectations for the grammatical number homonymy task.
score
65
Figure displays the cue contribution scores for the Whisper-medium model. Results show that the within-encoder scores are not noticeably higher than the ones for the randomly initialized model, except for layers 19-21 and 23-24 which show slight increase. In contrast, the cross and within- decoder scores show that the decoder prominently incorporates the cue word into its contextualized representation of the target word.
bayar
encOOer -undom
STOIA random
CNcoder random
20-
42-
ABT
In the decoder, each layer has two options for information integration (cf. Eq. 5): attending to previously generated tokens or attending to the au- dio representations output from the last encoder layer. By comparing the cross and within-decoder alignment scores, we find that the representations in the decoder strongly capture the syntactic depen- dency on the cue word through the self-attention module. We conjecture that in the encoder-decoder model, the decoder plays crucial role in captur- ing syntactic dependency and does not rely cue word representations in the encoder, probably due to having access text-token representations as an casier alternative. We examine the role the cross- attention module more carefully in Section 5.3.
Figure 2: Layer-wise cue contribution according to dif- ferent analysis methods averaged over all examples for Whisper-medium, trained (top) randomly initialized (bottom..
For all these scores, predictions made by Value Zeroing and Attention Norms methods are largely consistent with each other, with Value Zeroing sug- gesting a stronger reliance on cue word in the de- coder of the trained model. In contrast, raw atten- tion scores are uniform and less informative, as shown also in previous studies (Kobayashi et al., 2020; Hassid et al., 2022; Mohebbi et al., 2023). Figure 3 shows the context-mixing map for

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1.0
C100001
>|transcribe|>
<|notimestampss>
0.8
On
52-
y
trouve
0.6
un
Whiaper-mm uppl
è
0.4
X15%33
rop
weights
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0.2
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trouve
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0.0
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<|startottrascripg
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<|transcribe
<|natimestampa
0.5
0.50
Figure 4: Accuracy of probing classifiers trained on frozen target representations obtained from various ASR models. The depth of Whisper-base (6) and Whisper- small (12), has been normalized to facilitate com- parisons.
Figure 3: Within-decoder Value Zeroing context mix- ing map for Whisper-medium at layer for our running example. Target word 'trouve' representations notably rely on the cue word 'On' in the context. Similarly, the cue word 'un' notably contributes to forming represen- tations of the word 'aéroport'.
classifier to predict grammatical number.
Encoder representations in encoder-based and encoder-decooeer-bsse models, however, display two distinct patterns. Whisper encoders show a gradual increase as we progress through the layers. The encoder of Whisper-medium shows jump layer 20, the same layer for which observed significant cue contribution score in Section 5.1. Comparing Whisper models with different sizes, we can see that the gap between encoder and de- coder decreases as the model size increases. In contrast, XLSR-53 and XLSR-1 display notice- able spike in the middle layers (notably at layer 10), the same layers for which observed higher cue contribution scores. This implies that the target words in the middle layers of these models signifi- cantly incorporate the cue words in their represen- tations and encode number. This information can be transferred to the final layer with minimal loss, where the model is able to correctly disambiguate the target word.
layer 10 for example from the dataset: "On y trouve aéroport.", illustrating the dependence of target subword tokens the word in the decoder prefix.
5.2 Probing number encoding
Since the main source of information for disam- biguating the target word is its number agreement with the cue word, we hypothesize that layers with higher cue contribution score encode num- ber information more strongly and/or accessibly. To test our hypothesis, we conduct probing ex- periment by extracting target word representations across all layers, and associating each representa- tion with Singular or Plural label. We then train Logistic Regression classifier with L2 regular- ization these representations to predict target grammatical number.
5.3 Silencing the words
Results the previous experiments in Section 5.2 suggest that in encoder-decoder models, target rep- resentations in the decoder show a stronger encod- ing of syntactic dependency on the cue word com- pared to encoder representations. Additionally, our analysis of the context mixing scores in Section 5.1 revealed that encoder-decoder models exploit the agreement information provided by the cue word via its text representation in the decoder, rather than relying on the encoded spoken cue representation
Results. Figure presents 3-fold cross- validation accuracy of the probing classifiers for different models across all layers. As a baseline, we also include the results for target representations obtained from randomly initialized models.
For Whisper decoders, probing accuracy is close one indeed the accuracy is very high even for randomly initialized target models suggesting that simple word identity is being used by the diagnostic

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word in the encoder input does not significantly affect the model's confidence when generating the target word. However, if we blank out the cue to- ken in the decoder imput while keeping the encoder input intact, the model experiences a significant decrease in confidence in its target word predic- tion. The same drop can also be observed when both the encoder and decoder inputs have the cue words silenced/blanked out. These findings support our hypothesis that in encoder-decode models, the decoder captures the syntactic dependency on the cue words the target representations by attending to the cue word from its prefix rather than relying on the encoded cue words from the encoder. The scenario for encoder-based models, however, is dif- ferent. As we can see, silencing the cue words in the encoder input leads to significant drop in the model's confidence in its target word predic- tion. This corroborates with the results observed in Sections and 5.2.
cnooes dcoode
choodbrenty
probabiity
condition
Figure Changes in the models' confidence in gen- erating the target word in different setups of input ablations, averaged over models within each group. SC=Silence cue, BC=Blank cue, SBC=SSience & blank cue. ST=Silence target.
via the cross-attention module.
In this section, we employ the concept of input ablation (Covert et al., 2020) to assess the influence cue words on an ASR model's decision: we mea- sure how much the model's confidence drops when the cue word is excluded from the input during the transcription of ambiguous target words. For en- coder models, we silence the frames corresponding the cue word in the raw audio and provide the modified audio input to the model. For encoder- decoder models, we consider the following condi- tions: silencing the cue words from the raw audio input (silence cue), replacing the cue token with an unknown token in the input of the decoder (blank cue), and combining both modifications simultane- ously (silence & blank cue). As control we also include the condition where silence the target word in the input to the encoder (silence target) in this case we expect the models' confidence drop irrespective of architecture, as this manipulation removes most of the information available to the model about the identity of the target word.
Conclusion
In this paper, adapted state-of-the-art context mixing methods from the text domain analyze sensitivity syntactic cues and to trace informa- tion mixing Transformer-base models of speech recognition. Our analyses revealed that in encoder- only models, spoken cue-word representations di- rectly contribute to the formation of target word representations. In contrast, encoder-decoder mod- els largely relegate the task of capturing contextual dependencies to the decoder module. Through series of complementary experiments, we identi- fied two distinct roles performed by the decoder in encoder-decoder speech models when forming contextualized representations: attending to dis- crete prefix cues using self-attention, and attending to encoded audio from the encoder using cross- attention.
Our study validates the applicability of the adapted context mixing scores that were originally developed for text, and showcases their potential for discovering patterns of information flow in Transformer models of language in spoken modal- ity. We hope this convergence in analytical method- ologics between speech and text communities pave the way for developing unified models for nat- ural language processing (Chrupala, 2023). In line with previous research, our results discredit raw at- tention weights as a reliable source of information for information mixing in speech models.
We compute the drop in model's output probabil- ity as p(yr e) - p(zele))e (e:), where yt denotes the logit value corresponding to the target word time step f, and refers to given raw input audio at encoder setup, and original previously generated tokens at decoder setup.
Figure 5 presents the results for differ- ent ASR models, averaged over each model family. Notably, in encoder-decoder models (models in the Whisper family), we can see that silencing the cue
Results.

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7 Limitations
for self-supervised learning of speech representations. Advances in Neural Information Processing Systems. 33: 18::1249-1122 12460.
Our experiments rely on a specific phenomenon in French, as it provides a discrepancy between mark- ing of number agreement in spoken versus written forms, and therefore provide with an ideal case study for investigating context dependency and in- corporating syntactic cues. However, in the case of homophony in French, there one single cue word for each target word representation. In the future, we must search for other phenomena that allow us to investigate the role of multiple (converging or contrasting) sources of information when disam- biguation is needed.
Grzegorz Chrupata. 2023.
Putting natural in natural language processing. In Findings of the Associa- tion for Computational Linguistics: ACL 2023, pages 7820-7827, Toronto, Canada. Association for Com- putational Linguistics.
Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning. 2019. What does BERT look at? an analysis of BERT's attention. Pro- ceedings of the 2019 ACL Workshop BlackboxNLPP Analyzing and Interpreting Neural Nenworks for NLP, pages 276-286, Florence, Italy, Association for Com- putational Linguisties.
When choosing our target models, we have fo- cused on models that are trained for automatic speech recognition. Models trained according to different objectives must also be considered, and the impact of pre-training vs. fine-tuning on con- text dependency and information mixing must be examined.
Alexis Conneau, Alexei Baevski, Ronan Collobert, Ab- del rahman Mohamed, and Michael Auli. 2020. Un- supervised cross- pross-lingual representation learning for speech recognition. In Interspeech.
Ian Covert, Scott M. Lundberg, and Su-In Lee. 2020. Explaining by removing: A unified framework for model explanation. J. Much. Learn. Res., 22:209:1- 209:90.
It is also worthwhile use our adapted context- mixing methods to explore unsuspected phenom- ena in speech Transformers, without relying on strong prior hypotheses about model behavior.
Solène Evain, Ha Nguyen, Hang Le, Marcely Zanon Boito, Salima Mdhaffar, Sina Alisamir, Ziyi Tong, Natalia A. Tomashenko, Marco Dinarelli, Titouan Parcollet, A. Allauzen, Y. Estève, Benjamin Lecou- teux, François Portet, Solange Rossato, Fabien Ringeval, Didier Schwab, and Laurent Besacier. 2021. Lehenchmark: A reproducible framework for as- sessing self-supervised representation learning from speech. In Interspeech.
Acknowledgments
This publication is part of the project InDeep: Inter- preting Deep Learning Models for Text and Sound (with project number NWA.1 19.3.99 of Na- tional Research Agenda (NWA-ORC) programme. Funding by the Dutch Research Council (NWO) is gratefully acknowledged. We also thank SURF (www. surf. nl) for the support in using the Na- tional Supercomputer Snellius.
Javier Ferrando, Gerard Gállego, and Marta Costa- jusså. 2022. Measuring the mixing of contextual information in the transformer. In Proceedings of the 2022 Conference on Empirical Methods in Nat- ural Language Processing, pages 8698-87 14, Abu Dhabi, United Arab Emirates. Association for Com- putational Linguistics.
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B Cue contribution for other model sizes
The pattern of the results for layer-wise cue contri- bution is the same among all model sizes and other models within each family.
abs/2305.17733.
A Number homophony extraction procedure
trained
Besides matching our defined templates based on dependency trees, considered supplemental set of conditions ensure homophony exists in our constructed data.
13-
6-
Score
Irregular nouns in the list [oeil, yeux, aieul, aleux, ciel, cieux, vieil, vieux] well as nouns that end with al ail are filtered out as target noun.
layar
Secoder random
11-
Target verbs must be in the present or imper- feet tense, in any person- person-number except for 1-plural and 2-plural.
6.
Target verbs in their different number, person, and tense must end with specific letters en- sure they are pronounced the same their dif- ferent forms, In the present tense, a verb must end with 2-singular, 3-singular, and 3-plural, respec- tively, while in the imperfect tense, the verb must end with ais, aís, ait, and aient in their I-singular, 2-singular, 3-singular, and 3-plural, respectively. To check for this condition we utilized inflecteur library? which based on the DELA dictionary
AB
Figure B.1: Layer-wise contribution according different amalysis methods averaged over all examples Whisper-small, trained (top) vs. randomly initial- ized (bottom).
and ent in their I-singular,
24-
21
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10.
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Figure B.2: Layer-wise contribution according to different analysis methods averaged over all exam- ples for XLSR-1, trained (left) vs. randomly initialized (right).
Figure A.I: Layer-wise cue contribution according to different analysis methods averaged over all examples for Whisper-bast trained (top) vs. randomly initialized (bottom).
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Figure C.I: Within-encoder Value Zeroing context mixing map XISSR-33 layer for our running example.
Figure C.3: Within-encoder Value Zeroing context mixing map for Whisper-medium at layer 10 for our running example.


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Figure C.4: Cross Value Zeroing context mixing map for Whisper-medium at layer 10 for our running exam- ple.


Reviews:
[{"review_id": "l9Te5hWD7z", "reviewer": "Reviewer_vPyo", "summary": "", "questions": "Questions:\n\nA. [Lines 305-310] Why do the authors just use the test set from the Common Voice Spoken corpus? Was the training set not available? Was it due to resource constraints (If so then it would be good to mention it).\n\nB. [Lines 307-310] How exactly the dependency trees are used to discover instances of the templates mentioned in Lines 297-303?\n\nC. [Lines 352-356] Is there a reason to use two different variants of the Wav2vec2 family? also, what is the reason to use one multi-lingual variant and one variant pre-trained on French speech? \n\nD. [Lines 361-366] What is the reason for using Montreal Forced Aligner to extract start and end time, is does this give the best alignment results?\n\nE. [Lines 419-420] Figure 1 illustrates layer-wise context-mixing score maps for just the XLSR-53 variant of the encoder-only model, and this statement is generalized for both the models, so does XLSR-1 give the same results? do both have high scores for the middle layer or is there some variance? This statement is premature/partially wrong to conclude if the paper does not provide evidence of both models having the same trends.\n\nF. [Lines 438-443] Same as question (E), This statement is premature/partially wrong to conclude if the paper does not provide evidence of all model variants having the same trends.\n\nG. [Lines 410-412] Why is random initialization the baseline of comparison of the layer-wise scores in Figure 1 \\& 2? Why is the pre-trained weight initialization of these models considered baseline? It is not clear why random initialization is chosen as the baseline for comparison.\n\nH. In Figure 3., it is not clear to me how only the cue word has a high context-mixing map score with the target word. From the context-mixing map, I can see that the cue word even has a high score with the token \"alors\" which is not part of the target word. Why is it so?\n\nF. Section 2 discusses all the related works in analyses of speech models and context-mixing separately, but it is not clear whether there has been prior work (with or without Attention Norms and Value Zeroing ) done to use context-mixing methods for analyses of speech models (especially the phenomenon of homophones).", "limitations": "", "rating": 4, "confidence": 4, "soundness": 5, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Many thanks for the detailed concerns you raised. We believe we can address the concerns, and hope to reassure you about the generalizability and strength of our findings.\n\n\n**[Single phenomenon]**\\\nThe reason for choosing homophony in the French spoken language was that we wanted to conduct controlled experiments to evaluate the applicability of context-mixing methods for speech Transformers. It provides a strong hypothesis regarding which part of the context is relevant when transcribing ambiguous target words. In contrast, tasks involving polysemy lack such decisive hypotheses due to the potential disambiguation of the target word through both semantic and syntactic cues. Even though we did not limit the analysis to a specific template and covered various types of syntactic templates in which homophony may appear.\n\nAdditionally it is worth pointing out that there are many highly regarded studies which are limited to a single phenomenon in a single language, for example works investigating how language models capture long-distance subject-verb agreement. As long as that language is English, this narrow focus is usually not taken as a shortcoming. We would suggest that this type of paper is valuable, regardless of whether the language under consideration is English, French or any other language, if one makes a well-motivated choice (as we do) to study a particular phenomenon in that language, rather than trying out very many different languages and reporting only the results where the hypothesis holds (which we, emphatically, did not do).\n\n**[Training hyperparameter]**\\\nWe did not train the models ourselves (since training these pre-trained models is computationally very expensive). For the purpose of interpretability, we opted for off-the-shelf and state-of-the-art Transformer-based models. The training procedure for these models has been described in detail in Section 4.2. For specific training hyperparameters, please refer to the original papers cited in our work.\n\nIn contrast to Whisper models, which have already been trained on the ASR task, the models in the Wav2vec2 family are pre-trained in a self-supervised manner. Therefore, they need to be fine-tuned for ASR. We used the commonly-used fine-tuned versions of these models from the HuggingFace hub, which are robustly and standardly fine-tuned, and have the highest download rate. For detailed fine-tuning hyperparameters, please refer to the footnotes 5 and 6 mentioned in our paper.\n\n**[Consistant results for other model sizes, and other models within a family in Figures 1 and 2]**\\\nFigures 1 and 2 display the results only for XLSR-53 and Whisper-medium models which have 24 layers. In contrast to the other figures, it was difficult to integrate the results for all model sizes into one figure here. However, we assure the reviewer that the pattern of the results is the same among all models within each family. Our findings have been derived based on all the models used in the analysis. We will include the results for the other model sizes in the Appendix.\n\n\n**[QA]**\\\nWe only used the test split of the Common Voice corpus. This decision was made because the training and validation sets have already been utilized during the fine-tuning phase of models within the Wav2vec2 family. Therefore, the use of these sets would introduce bias and impede fair comparisons between models.\n\n**[QB]**\\\nUsing the SpaCy toolkit, it's possible to extract the dependency tree of an utterance and find the noun (verb) corresponding to a determiner (pronoun). After matching the template, we use the procedure described in Appendix A for homophony extraction. We refer the reviewer to the corresponding codes in the 'data_generator.py' file in our supplementary materials for more details.\n\n**[QC]**\\\nWe aimed to encompass a wide range of off-the-shelf speech Transformer models in our experiments to make our findings more generalized and comprehensive. For example, we observed the identical pattern of context mixing for both the multilingual and monolingual variants of Wav2vec2, validating the applicability of context mixing methods employed in our study for speech Transformers, regardless of the language of their training data.\n\n**[QD]**\\\nThe Montreal Forced Aligner consistently delivers more accurate results than available alternatives and its effectiveness has been demonstrated in previous works as well [1].\\\n[1]: https://openreview.net/forum?id=AvcfxqRy4Y\n\n**[QE, QF]**\\\nThe results for XLSR-1 are the same as XLSR-53 (high scores in middle layers). Also, the results for other model sizes in the Whisper family are the same as for Whisper-medium (low scores in the encoder and high scores in the decoder). Therefore, we assure the reviewer that our observations are consistent across models within a family.\n\n**[QG]**\\\nNon-trivial patterns can often be found in representations from randomly initialized models. These are due to systematic propagation of the input representation through the network, and are not due to any learning effect. In order to control for this phenomenon, it is crucial to use randomly initialized models as a reference point in interpretability studies.  \n\n**[QH]**\\\nPlease note that the models are not specifically trained for homophony disambiguation. Therefore, the model can potentially encode different types of knowledge. Although it's not possible to draw conclusions based on only one example, the high-value contribution of token \"Il\" in the construction of token \"alors\" might be due to capturing other kinds of dependencies between these two words in the utterance.\n\n\n**[QF]**\\\nTo the best of our knowledge, this is the first work employing context-mixing methods for analyses of speech Transformers. Please let us know if you know of any previous efforts.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The paper investigates an interesting phenomenon of homophony in the spoken French language. It demonstrates the applicability of state-of-the-art context-mixing methods developed for the textual domain to study and interpret the influence of cue words in speech transcription tasks. Their study helps in concluding that cue words play an important role in properly transcribing spoken languages containing homophones (French in this work). The findings from the paper suggest that the decoder in the encode-decoder model plays a significant role in capturing syntactic dependencies, which has the potential use to train spoken language models in a parameter-efficient manner to tackle the problem of homophony in spoken languages. The layer-wise attention score visualizations provided in this work make it highly interpretable, and the cue contribution probing experiments validate their main findings.", "reasons_to_reject": "Although the majority of concerns are addressed in the limitation section, the major concerns that I have are:\n\n1. The study only focuses on a single phenomenon of Homophones and that too using a single language (French), which makes me question the applicability and generalizability of the text-based context-mixing methods for other spoken languages and phenomenons like polysemy (in speech transcription).\n\n2. Although the authors state to release the code in the future, the paper does not have much information regarding the training hyperparameter (No section that discusses the training procedure of the models), like how long were the models trained and were these models trained till convergence to confidently conclude the final finding based on the provided layer-wise context-mixing score maps? Otherwise, it would be hard to trust these findings as there is no discussion on whether the results will remain the same irrespective of how long the model is trained.\n\n3. The layer-wise context-mixing score analysis maps are only provided for single encoder-only and encoder-decoder model variants (Figure 1 \\& Figure 2), which in my opinion makes it premature to conclude the prior hypothesis in a generalized statement (Line 419-420). If similar behavior exists for other variants then I believe their results should also be included in the paper to justify a generalized conclusion.\n\nOverall although they have a descent analysis on using text-based context-mixing methods in the spoken language setting, I believe that more thorough justifications and evidence are needed to trust their claims.", "questions_to_authors": "Questions:\n\nA. [Lines 305-310] Why do the authors just use the test set from the Common Voice Spoken corpus? Was the training set not available? Was it due to resource constraints (If so then it would be good to mention it).\n\nB. [Lines 307-310] How exactly the dependency trees are used to discover instances of the templates mentioned in Lines 297-303?\n\nC. [Lines 352-356] Is there a reason to use two different variants of the Wav2vec2 family? also, what is the reason to use one multi-lingual variant and one variant pre-trained on French speech? \n\nD. [Lines 361-366] What is the reason for using Montreal Forced Aligner to extract start and end time, is does this give the best alignment results?\n\nE. [Lines 419-420] Figure 1 illustrates layer-wise context-mixing score maps for just the XLSR-53 variant of the encoder-only model, and this statement is generalized for both the models, so does XLSR-1 give the same results? do both have high scores for the middle layer or is there some variance? This statement is premature/partially wrong to conclude if the paper does not provide evidence of both models having the same trends.\n\nF. [Lines 438-443] Same as question (E), This statement is premature/partially wrong to conclude if the paper does not provide evidence of all model variants having the same trends.\n\nG. [Lines 410-412] Why is random initialization the baseline of comparison of the layer-wise scores in Figure 1 \\& 2? Why is the pre-trained weight initialization of these models considered baseline? It is not clear why random initialization is chosen as the baseline for comparison.\n\nH. In Figure 3., it is not clear to me how only the cue word has a high context-mixing map score with the target word. From the context-mixing map, I can see that the cue word even has a high score with the token \"alors\" which is not part of the target word. Why is it so?\n\nF. Section 2 discusses all the related works in analyses of speech models and context-mixing separately, but it is not clear whether there has been prior work (with or without Attention Norms and Value Zeroing ) done to use context-mixing methods for analyses of speech models (especially the phenomenon of homophones).", "paper_topic_and_main_contributions": "This paper focuses on using context-mixing methods built for the textual domain to investigate and analyze the influence of syntactic cues in disambiguating spoken words with similar pronunciation but different transcription in spoken language models. They use the phenomenon of homophony in the French to analyze the importance of syntactic cues in two types of transformer-based automatic speech recognition (ASR) models, (1) Encoder only where they experiment on models from Wav2vec2 family, and (2) Enoder-decoder models where they experiment on models from Whisper family. They extract the audio-transcription samples from the Common Voice Spoken corpus test set based on specific syntactic templates that satisfy the prior expectation of samples having homophony in French. Investigation of context-mixing on different transformer-based models (encoder only and encoder-decoder models) on these samples is done by visualizing per layer context-mixing scores for the cue words using state-of-the-art context-mixing methods namely, (1) Attention Norm, and (2) Value Zeroing. From their experiments, they were able to confirm the prior expectation of dependence of the target word transcription on their corresponding syntactic cue words. The main finding from the context-mixing scores for both the context-mixing methods was that in the encoder-decoder models, the syntactic dependency is captured primarily in the decoder part of the model. They further validated their claim by conducting experiments by training a simple classification model from the extracted target word representations for all the layers to predict the target grammatical number (binary classification with labels singular/plural), where the results implied that the middle layers primarily incorporated information to encode the number and syntactic cue representation. Their work establishes the applicability of the context-mixing methods developed for the textual domain to be used in the spoken domain for the interpretability of context influence and information flow.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Missing_References": "Section 2 discusses all the related works in analyses of speech models and context-mixing separately, but it is not clear whether there has been prior work (with or without Attention Norms and Value Zeroing ) done to use context-mixing methods for analyses of speech models (especially the phenomenon of homophones).", "Typos_Grammar_Style_and_Presentation_Improvements": "[Lines 419-420, 438-443] Figure 1 \\& 2 should be modified to include results for all the variants of both the family of models for proper comparison.\n\n[Lines 419-420, 438-443] Figure 1 \\& 2 should also include the context-mixing score maps for pre-trained weights along with the randomly initialized as well as fine-tuned weights.\n\nFigure 3 should be modified with some sort of highlighting to better understand how the cue word has a high context-mixing map score with the target word. \n\nIn Table 1, it is unclear for a person who does not know the French language to understand the example properly, so it would be helpful to include the English translation in the examples."}}, {"review_id": "v3GfemOO75", "reviewer": "Reviewer_AWuy", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "soundness": 5, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for showing interest in our work. We kindly request that you elaborate on the specific aspects of our study that contributed to your score. Your score is greatly appreciated.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The topic of the paper is related to the conference. It is written clearly and there are no issues with the technical parts of the work. Overall the contribution of the paper is sound and highly recommend for acceptance.", "reasons_to_reject": "There are no major limitations that would cause any risk of presentation.", "questions_to_authors": "", "paper_topic_and_main_contributions": "The authors analyzed various context-mixing methods to see whether the existing speech transformer models look for syntactic cues for the correct transcription of homophones in French. The main contribution of the paper is toward explainable AI where we can analyze different transformer architectures in regard to the encoded speech representations. The results show that both encoder-only and encoder-decoder architectures attend to syntactic cues regarding the homophones in various layers in either the encoder layer of the former and the decoder or cross layers of the latter. ", "excitement": 4, "reproducibility": 5, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "03gna9rqBa", "reviewer": "Reviewer_RLmt", "summary": "", "questions": "1. I think the example on 058-069 may contain an error. On 067, the authors state \"here the singular determiner\", but the determiner in their example on line 059 (_les_ livres) is plural.(Also, the small caps on Target and Cue on lines 060 and 067 do not appear to be used again in the text.)\n\n2. Given the thoroughness of the surrounding description, the lack of mention/description of d_h in Equation (4) looks like an omission.\n\n3. Does the fact that data are limited to only those cases in which all models produce an accurate transcription of Cue and Target(e.g. 395-396)  introduce a bias into the findings? It seems that the expectation voiced on lines 400-402 may not be obviously substantiated.\n\n4. If the \"score\", \"20\", \"10\", etc textboxes at the right of Figures 1 and 2 are intended as some sort of legend, they are not onvious or sufficiently visible to be useful.\n\n5. The example on 453-457 merits more explanation than the authors provide. The key points are likely to be missed by most readers.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 5, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**[Q3]**\\\nEvaluation-wise, we cannot expect a model to attend to the relevant part of the context of the label when it is unable to generate the true label. This is why we followed [1] and [2] and evaluated the model only on correctly classified examples. This choice does not pose a bias in our findings, as the performance of the models used in our study is impressively high for the task of ASR, and only a few samples have been filtered out.\\\n[1]: https://arxiv.org/abs/2301.12971 \\\n[2]: https://arxiv.org/abs/2304.14767\n\n**[Q1,2,4,5]**\\\nWe would like to thank you for pointing out the typos and presentation points.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "Both the authors' findings and the method of obtaining them are interesting and compelling.", "reasons_to_reject": "None. There are some minor points meriting comment or correction, see \"Questions For The Authors\" below.", "questions_to_authors": "1. I think the example on 058-069 may contain an error. On 067, the authors state \"here the singular determiner\", but the determiner in their example on line 059 (_les_ livres) is plural.(Also, the small caps on Target and Cue on lines 060 and 067 do not appear to be used again in the text.)\n\n2. Given the thoroughness of the surrounding description, the lack of mention/description of d_h in Equation (4) looks like an omission.\n\n3. Does the fact that data are limited to only those cases in which all models produce an accurate transcription of Cue and Target(e.g. 395-396)  introduce a bias into the findings? It seems that the expectation voiced on lines 400-402 may not be obviously substantiated.\n\n4. If the \"score\", \"20\", \"10\", etc textboxes at the right of Figures 1 and 2 are intended as some sort of legend, they are not onvious or sufficiently visible to be useful.\n\n5. The example on 453-457 merits more explanation than the authors provide. The key points are likely to be missed by most readers.", "paper_topic_and_main_contributions": "The article describes a controlled analysis of the speech transformer model in an attempt to shed light on the mechanism by which such models build their representations. The analysis focuses on problems posed by homophony in French, and explores both state-of-the-art encoder-only systems and state-of-the-art encoder-decoder systems.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "line 584: write -> written"}, "extra_text": {}}, {"review_id": "wBr1Hyx3Lx", "reviewer": "Reviewer_EiPu", "summary": "", "questions": "How do you ensure that the context mixing methods are robust and reliable for speech models, especially when dealing with noisy or low-quality audio inputs?\nHow do you handle cases where there are multiple cues or conflicting cues for disambiguating a target word in speech?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**[Focusing on encoder block]**\\\nWe do not, in fact, focus just on the encoder block. Our experiments are conducted on both the encoder and decoder blocks of Transformers, considering every possible type of information interaction to mix the context: within-encoder, within-decoder, and cross-attention. However, analyzing context mixing in models with different training objectives, such as Clip or Diffusion models (which are based on Transformers as well but beyond the scope of this study), would be an interesting future direction.\n\nCopying answer to **[Single phenomenon]** for convenience:\\\nEven though we opted for a single controlled task, we did not limit the analysis to a specific template and covered various types of syntactic templates in which homophony may appear.\nAdditionally it is worth pointing out that there are many highly regarded studies which are limited to a single phenomenon in a single language, for example works investigating how language models capture long-distance subject-verb agreement. As long as that language is English, this narrow focus is usually not taken as a shortcoming. We would suggest that this type of paper is valuable, regardless of whether the language under consideration is English, French or any other language, if one makes a well-motivated choice (as we do) to study a particular phenomenon in that language, rather than trying out very many different languages and reporting only the results where the hypothesis holds (which we, emphatically, did not do).\n\n**[Q]**\\\nOur templates are specifically designed to make sure that there is a single cue which determines the spelling of the target with no ambiguity. In our experiments we use clean speech so poor quality audio is unlikely to be a major factor in the results. Additionally we also make sure to analyze examples where the models generate the correct output transcription to make sure that the model is capturing the phenomena in question.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "It adapts state-of-the-art context mixing methods from the text domain to speech models and validates their applicability.\nIt reveals that encoder-only models rely on spoken cue-word representations to disambiguate target words, while encoder-decoder models mainly use text cue-word representations in the decoder prefix.", "reasons_to_reject": "It only focuses on the encoder block of the Transformer architecture, and does not consider the decoder block or other components such as the output layer or the loss function. The authors acknowledge this limitation in their conclusion and suggest future work to extend their analysis to other parts of the model. Another possible weakness is that the paper relies on a specific linguistic phenomenon (homophony in French) to probe context mixing in speech models, which may not generalize to other languages or tasks. ", "questions_to_authors": "How do you ensure that the context mixing methods are robust and reliable for speech models, especially when dealing with noisy or low-quality audio inputs?\nHow do you handle cases where there are multiple cues or conflicting cues for disambiguating a target word in speech?", "paper_topic_and_main_contributions": "The paper is an interesting and novel study of how speech models learn syntactic dependencies and handle homophony. It also showcases the potential of context mixing methods for discovering hidden phenomena in speech Transformers.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper investigates how syntactic cues affect the transcription task of homophones in speech models. It adapts various context-mixing methods from the text domain to study this phenomenon. The work primarily focuses on homophony in the French language, utilizing both encoder-only and encoder-decoder models for analysis. 
Overall, the reviewers find the topic interesting and the paper well-written with clear technical aspects. The reviewers do not have major concerns but they concern about limited focus on a single phenomenon of homophones and that too using a single language (French).
Decision: Accept-Main