Gender Assignment in Speech Translation

• Gender assignment in speech translation is the process of generating grammatically correct, gender-specific outputs by inferring speaker and listener gender from non-marked source languages.
• Researchers use techniques like gender token integration, encoder–decoder modeling, and corpus enrichment to mitigate masculine bias and enhance translation accuracy.
• Practical implementations incorporate metadata tagging, coreference adaptation, and external language models to improve fairness and overall system performance.

Gender assignment in speech translation refers to the processes, challenges, and methodologies for generating morphosyntactically correct target-language forms that correspond to the speaker’s (and, where relevant, the listener’s) gender when translating from a language lacking explicit gender marking (such as English) into a language that marks gender (such as Arabic, French, Italian). This phenomenon is of critical importance in both neural machine translation (NMT) and speech translation (ST), with far-reaching implications for system fairness, user representation, and translation accuracy.

1. Linguistic Background and Core Challenges

Linguistic gender assignment becomes especially problematic when translating from a language that does not morphologically indicate gender to one that mandates explicit gender agreement on pronouns, adjectives, or verb forms. In English–Arabic, English–French, or English–Italian translation, the gender of the speaker (e.g., “I am happy”) or addressee (e.g., “Are you certain?”) must be inferred or supplied for the output to be grammatically and pragmatically correct.

The challenge is twofold:

• Ambiguity: English sentences often lack cues necessary for gender agreement, requiring the translation system to “guess” or default to a particular gender form, typically the masculine generic.
• Resource Scarcity: There is a scarcity of large-scale parallel corpora annotated for speaker and/or listener gender, especially in speech data. Existing text-based MT training data perpetuate masculine bias inherent in original corpora.

This leads to systematic misgendering, under-representation of feminine forms, and persistent alignment with entrenched social stereotypes rather than real-world gender distributions (ElAraby et al., 2018, Vanmassenhove et al., 2019, Bentivogli et al., 2020, Gaido et al., 2020, Mastromichalakis et al., 6 Mar 2025).

2. Model Architectures and Gender Integration Strategies

Several architectural strategies are used to address gender assignment:

• Encoder–Decoder NMT Models: Classic sequence-to-sequence models such as bidirectional LSTM encoders and attention-based decoders, with the context vector $c_i = \sum_j \alpha_{ij} h_j$ where $h_j$ is the encoder annotation vector and $\alpha_{ij}$ is a learned alignment score (ElAraby et al., 2018).
• Direct (End-to-End) ST Models: Process audio features (e.g., MFCCs or log-Mel spectrograms) through convolutional and attention-based layers, bypassing ASR transcription and thus preserving speaker-specific features like pitch and intonation (Bentivogli et al., 2020, Gaido et al., 2020).
• Cascade Systems: Separate ASR and MT components; while flexible, they often lose non-textual cues unless augmented with external gender tags or metadata.
• Multi-Gender and Gender-Specialized Models: Single models augmented with gender tokens (e.g., “<male>”, “<female>”) (Gaido et al., 2020, Gaido et al., 2023), or specialist models trained or fine-tuned per gender class.

Integration of gender metadata occurs by appending/prepending special tokens to the source or decoder input (ElAraby et al., 2018, Vanmassenhove et al., 2019, Gaido et al., 2023), or merging gender embeddings into encoder/decoder representations. Gradient reversal layers or discriminators can be used to enforce invariance to vocal gender cues in the internal representations (Gaido et al., 2023).

Inference-time solutions have also emerged, e.g., swapping out the internal LLM (ILM) with a gender-specific external LM via shallow fusion at decoding time, modulating the hypothesis score as

$$\hat{y} = \mathrm{argmax}_y \{\log p_{MB}(y|x) - \beta_{ILM}\log p_{ILM}(y) + \beta_{ELM}\log p_{ELM}(y)\}$$

where $p_{MB}$ is the base model posterior, $p_{ELM}$ the gender-specific external LM, and $p_{ILM}$ the internal LM estimate (Fucci et al., 2023).

3. Data Annotation, Corpus Generation, and Resource Construction

Robust gender assignment requires parallel data with explicit gender labeling for speakers or referents—a resource rarely available at scale. Several methodologies have been developed:

• Automatic Annotation and Rule-Based Tagging: For Arabic, gender dependencies are detected through POS tagging and predefined rules (e.g., finding context in adjectives after first-person pronouns, second-person verb forms, or Arabic calling particles), resolving ambiguities using the aligned English source when available (ElAraby et al., 2018).
• Projection via Word Alignment: Creating target gender annotations (TGA) by morphologically tagging the target language, aligning tokens (e.g., with fast_align), and projecting gender tags to corresponding source tokens, then incorporating these tags as additive factors during NMT training (Stafanovičs et al., 2020).
• Large-Scale Demographic Enrichment: Annotation of large parallel corpora—e.g., Europarl data are enriched with speaker demographics including gender and age to better inform model conditioning (Vanmassenhove et al., 2019).
• LLM-Based Debiasing: Utilizing GPT-4 with chain-of-thought prompting to correct MT-derived translations, generating parallel (masculine/feminine) target forms and using these as supervision for subsequent fine-tuning (Bansal et al., 10 Jan 2025).

Specialized benchmarks such as MuST-SHE and WinoST provide focused, balanced sets for rigorous evaluation of gender translation, including coverage of both natural and synthetic speech, detailed part-of-speech, and agreement chain annotations (Bentivogli et al., 2020, Costa-jussà et al., 2020, Savoldi et al., 2022).

4. Gender Disambiguation, Coreference, and Non-Binary Extension

Sentence-level tagging often overgeneralizes, causing gender “bleed” across multiple referents in complex utterances. To mitigate this:

• Word-Level Gender Inflection Tags: Tags such as “<$M$>”, “<$F$>”, or “<$N$>” (neutral) are inserted immediately after target entities, supported with synthetic adaptation data that illustrate both single and multi-entity scenarios (Saunders et al., 2020).
• Coreference-Adaptive Training: Multi-entity examples help the model to learn that gender tags apply locally, reducing the overgeneralization effect, and achieving strong accuracy improvements without corrupting secondary entity labeling (measured by $\Delta L_2$) (Saunders et al., 2020).
• Non-Binary Handling: Neutral tags and adapted placeholder markers for articles and inflections facilitate attempts at non-binary or gender-inclusive translation, though reported accuracy remains lower than for binary inflection (Saunders et al., 2020).

5. Evaluation, Bias Analysis, and Empirical Outcomes

Evaluation employs both intrinsic and extrinsic metrics:

• BLEU and Gendered BLEU: Standard BLEU remains informative, but must be combined with metrics that specifically assess gender accuracy for speaker-referential and listener-referential words (ElAraby et al., 2018, Bentivogli et al., 2020).
• Challenge Sets and Benchmark Scores: WinoST and MuST-SHE benchmark sets allow systematic measurement of gender bias and accuracy under controlled conditions. Gender accuracy in direct ST lags MT by over 23% (e.g., 51% vs 74.1% for En-De), and persistent gaps between masculine and feminine accuracy are observed (Costa-jussà et al., 2020).
• Bias Quantification: Probability-based metrics such as GRAPE (Gender Probability Difference, GPD) score system output against both normative and labor statistic baselines, quantifying deviation from balanced or real-world gender distributions in occupation assignment (Mastromichalakis et al., 6 Mar 2025). Persistent masculine bias is recorded across major MT and ST systems, even when real-world statistics would suggest differently.
• Probing and Interpretability: Attention-based probes on model hidden states reveal that traditional encoder–decoder architectures retain speaker gender information, whereas speech+MT systems with adapter layers tend to erase salient gender cues, leading to further masculine default in translation outputs (Fucci et al., 2 Jun 2025).
• Self-supervised Models and Data Balance: Downstream ST accuracy and fairness are sensitive to the integration method of self-supervised models (e.g., wav2vec 2.0). End-to-end fine-tuning benefits from gender-balanced pre-training, while feature-extractor usage yields more buffered impact (Boito et al., 2022).

6. System Design, Practical Implications, and Mitigation Strategies

To address gender assignment shortcomings and minimize bias propagation:

• Explicit Metadata and Tagging: Systems are increasingly adopting external gender metadata, whether via runtime tags, POS-based classifiers to trigger gender-aware models, or fixed tokens in the decoder input (ElAraby et al., 2018, Gaido et al., 2023). Gradient reversal and adversarial training can inhibit models from over-relying on vocal cues, preserving fairness for speakers whose voice may not reflect their identified gender (Gaido et al., 2023).
• Inference-Time Control: Shallow fusion with external gender-specific LLMs at inference time allows for gender-corrected output without retraining, outperforming baseline and specialist models for feminine accuracy (Fucci et al., 2023).
• Three-mode Fine-tuned Models: Allowing modes for automatic, masculine, or feminine output supports scenarios where gender is predefined, unknown, or inferred—enhancing inclusivity, especially for non-binary or underrepresented user groups (Bansal et al., 10 Jan 2025).
• Streaming Speaker Embedding Approaches: Token-level t-vector embeddings enable simultaneous streaming speaker change detection and high-accuracy gender classification (up to 98.9%), crucial in multi-talker environments and downstream text-to-speech applications (Wang et al., 4 Feb 2025).
• Corpus and Algorithmic Adjustments: Combined segmentation (BPE + char-level) and linguistically motivated methods (Morfessor, LMVR) can significantly improve feminine inflection translation accuracy (gains up to 30% over BPE) (Gaido et al., 2021). Selection and curation of gender-balanced, well-aligned, and demographically rich training data are imperative.

7. Societal Implications, Open Problems, and Future Research

Systematic masculine bias remains prevalent across commercial and research ST/MT systems, driven by imbalanced corpora, architectural bias (information loss in adapters), and reliance on social stereotypes over real-world data (Mastromichalakis et al., 6 Mar 2025, Fucci et al., 2 Jun 2025). Ongoing concerns include:

• Sociolinguistic and Fairness Impact: Misgendering and under-representation not only reduces grammaticality but can diminish user experience, perpetuate stereotypes, and marginalize gender minorities. Direct applications (subtitle generation, TTS voice assignment, assistive tech) amplify these effects (Costa-jussà et al., 2020, Wang et al., 4 Feb 2025).
• Binary Gender Limitation: Current systems generally adhere to binary gender categories—a limitation actively highlighted as insufficient. There is increasing interest in models and language resources that are non-binary-inclusive (Saunders et al., 2020, Gaido et al., 2023).
• Design Recommendations: Human-centered corpus design and inclusive protocols, advanced debiasing techniques, adaptive model strategies, and diagnostic evaluation tools are recommended to ensure equitable outcomes (Seaborn et al., 2023, Ghosh et al., 2023).
• Technological Evolution: Future work is suggested in combining acoustic, lexical, and external metadata cues, extending inference-time strategies, refining adapter and fusion architectures, and probabilistic evaluation frameworks supporting ambiguous or multiple-gender outputs (Fucci et al., 2023, Fucci et al., 2 Jun 2025, Mastromichalakis et al., 6 Mar 2025).

Gender assignment in speech translation remains an active and evolving research area, requiring rigorous evaluation, holistic corpus design, and continual refinement of methods to ensure fair and accurate cross-linguistic representation.