Building a Production Greek-English Speech Recognizer

Christos Petrocheilos; Cleopatra Papadopoulou; Chris Porikis; Ioakeim Perros; Ayoub Kirouane; Themistoklis Nikolis

eess.ASSep 11, 2026

Building a Production Greek-English Speech Recognizer

Authors: Christos Petrocheilos, Cleopatra Papadopoulou, Chris Porikis, Ioakeim Perros, Ayoub Kirouane, Themistoklis Nikolis

Organizations: Sophea AI Lab, KIEFER SA, Athens, Greece

Abstract

We report a multi-month engineering program to build Sophea, a production bilingual Greek-English automatic speech recognition system. We evaluate the system against nine production gates covering Greek and English word error rate, language identification, and hallucinations on non-speech audio. Across twenty-three training iterations and two model architectures, no training-data composition passed all nine gates simultaneously. Meeting the Greek noisy-environment target required about 1,500 steps of dense domain exposure, while preserving English language identification tolerated only about 250 steps, or about 1,250 with a rebalanced mix that reduced Greek accuracy. We describe a six-stage data pipeline in which calibrating an audio-quality filter against in-domain anchors reduced the discarded share of scored Greek audio from 98.7 percent to 10.6 percent. A pre-registered ablation isolated a hallucination defect to one training-data package. A three-model ROVER ensemble increased gate coverage from 4-7 of 9 for individual models to 9 of 9 and reduced overlapping-speech WER from 53.35 percent to 37.87 percent, a 29 percent relative improvement. A separate learned per-clip arbiter over two models is listed as sophea/asr-k1 (preview) on the public Open ASR Leaderboard, with 4.26 percent average WER across eight public English test sets, and reaches 25.88 percent WER on live Greek noisy-environment traffic. We also document five cases in which a measurement tool produced a plausible but incorrect result and seven substantial approaches that were evaluated but not shipped. No model weights or training data are released; we report methodology and quantitative results only.

Figures & tables

Figure 1: Required versus tolerated training-step budgets for Greek noisy-environment exposure. The Greek noisy-environment gate needs

s\geq 1500

steps; the English language-identification gate tolerates

s\leq 250

(baseline mix) or

s\leq 1250

(rebalanced mix, at the cost of stalling Greek accuracy short of its own gate). The two intervals never overlap.

Figure 2: Share of scored Greek training rows an audio-quality filter would discard: a conventional absolute threshold against one calibrated to known-clean, in-domain reference recordings scored under the same model.

Figure 3: Production gates passed (of nine) across the compact model’s seven iterations (C1 to C7). C6 replaced machine-transcribed Greek labels with human-verified consensus labels and changed nothing else.

Gate	Threshold	Value	Result
Anchor WER	$\leq$ 3.09	3.03	PASS
Code-switch (cs)	$\leq$ 7.72	7.42	PASS
Meeting (ITN)	$\leq$ 14.85	14.79	PASS
Audio-LONG (ITN)	$\leq$ 12.77	10.42	PASS
Boilerplate (BP)	$\leq$ 18	26	FAIL
English WER (W-EN)	$\leq$ 2.63	2.49	PASS

Table 1: L4’s internal ship-decision gate battery, distinct from the nine production gates in Table 3 . Anchor WER is a frozen read-Greek regression probe; cs is a code-switching probe specific to this campaign, not the code-switched benchmark reported elsewhere; meeting and Audio-LONG are ITN-scored internal probes, the latter on content in the domain of the data source identified as an anchor toxin above; BP is a raw boilerplate hit count and the sole gate this checkpoint failed; W-EN is English word error rate on a 500-utterance LibriSpeech test-clean probe. None of these surfaces, including “meeting,” matches a benchmark used elsewhere in this paper: this table’s Meeting (ITN) value is not the “Greek, business meetings” row of Table 3 .

Model	FLEURS	FLEURS	LibriSpeech	Real-world	Community-	Parliamentary
	(English)	(Greek)	(clean)	Greek, agg.	sourced Greek	Greek
Large model	10.22%	9.13%	2.67%	15.97%	1.73%	20.81%
Turbo tier	13.02%	11.53%	2.46%	20.65%	21.61%	24.98%

Table 2: Word error rate, large production model versus the untrained turbo tier, across six evaluation surfaces (

n{=}200

for the three real-world Greek columns).

Figure 4: Word error rate by benchmark, large model versus the turbo tier. Turbo runs on stock weights plus decode-time tuning only; both fine-tuning attempts regressed code-switching and were not shipped.

Benchmark	Ours	Canary	Whisper	Two-model	Three-model
		FT	FT	vote	vote
Greek, clean read speech (FLEURS)	8.99	8.63	5.23	6.96	3.98
Greek, TEDx talks	13.29	6.44	6.73	6.99	6.01
Greek, community-sourced speech	8.70	8.70	1.63	7.07	2.72
Greek, informal conversational speech	9.64	8.58	7.85	7.70	6.93
Greek, business meetings	20.53	18.27	15.73	17.55	16.59

Table 3: Word error rate (lower is better) and language-identification accuracy (higher is better, in percent) across three independently trained recognizers and their confusion-network combinations, on the nine production benchmarks plus five additional public and internal evaluation surfaces. “Ours” is the production bilingual model of Section 5.1 ; “Canary FT” and “Whisper FT” are Greek fine-tunes of NVIDIA Canary and Whisper large-v3 respectively, used only as ensemble members here, not as production models in their own right. Bold marks the best of the five columns shown per row. All entries are single-run point estimates with no confidence intervals; differences between columns smaller than roughly one WER point should not be treated as established (Section 11 ).

Figure 5: Sophea ASR K1 against its two individual members, average word error rate over the seven cleaned test sets of Table 4 , scored with the public Open ASR Leaderboard’s evaluation harness (K1 listed on the live board as sophea/asr-k1 (preview) since 11 September 2026).

Test set	Sophea ASR K1	Qwen far-field variant alone	Canary FT alone
AMI	8.50	8.19	13.86
AMI, close-talk	7.29	7.27	11.63
Earnings-22 (cleaned, AA-chunked)	6.03	8.11	9.64
GigaSpeech	7.69	7.85	8.08
GigaSpeech, clean	7.65	7.80	7.96
LibriSpeech, test-clean	1.18	1.38	1.25

Table 4: Word error rate on the public Open ASR Leaderboard’s standard English test sets, using the leaderboard’s own evaluation harness, forced-English decoding; submitted as PR #201 to the leaderboard’s public repository and listed on the live board as sophea/asr-k1 (preview) since its 11 September 2026 release. “Canary FT alone” is the same fine-tune as Table 3 ; the “Qwen far-field variant” is a separate LoRA repair of the compact model (Section 5.1 ), not the C6 checkpoint used elsewhere in this paper. “Sophea ASR K1” is the learned-arbiter combination of the two, described above. Both averages use the same seven test sets for all three systems, with Earnings-22 entering as its cleaned AA-chunked split throughout, since that is the only Earnings-22 split scored for the two members (K1’s raw Earnings-22 from our own run is 8.22). The canonical seven-set average uses the raw AMI, GigaSpeech, and VoxPopuli splits, the leaderboard’s original composite; the cleaned seven-set average substitutes each dataset’s close-talk, cleaned, or AA-chunked split where the leaderboard provides one. The live board’s listed 4.26 for K1 is the board’s own eight-set average, which adds Voice Arena Monsoon (3.66), a set the two members were not scored on. Bold marks where Sophea ASR K1 leads both individual members by more than the roughly one-WER-point noise floor discussed below; entries within that floor (e.g. VoxPopuli, clean-accent) are left unmarked even where the raw numbers nominally favor one side. All entries are single-run point estimates with no confidence intervals; differences smaller than roughly one WER point should not be treated as established (Section 11 ). The AMI and AMI, close-talk rows additionally carry the Canary fine-tune’s confirmed AMI-pretraining exposure noted in Section 6.3 , capping their external validity as an unseen-data comparison.

Figure 6: Word error rate on overlap-only speech segments from real meeting recordings, single model versus two-model confusion-network voting.

Automatic Speech Recognition

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Deterministic Prompting for Speaker-Stable Low-Resource Greek TTS

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Institute for Language and Speech Processing, Athena R.C., Greece · Department of Digital Medicine, University of Bern, Switzerland · School of Electrical and Computer Engineering, National Technical University of Athens, Greece

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Modern TTS systems approach human quality for high-resource languages but degrade when clean speech data is scarce. Modern Greek exemplifies this, lacking the curated corpora behind state-of-the-art synthesis. We propose a data curation recipe that transforms audiobook recordings into TTS-ready data via WhisperX alignment and filtering. Then we fine-tune Parler-TTS (880M), a prompt-based multilingual model whose pre-training encodes phonetic priors transferable to Greek. During development, we find that LLM-generated style prompts introduce speaker drift at inference. Replacing them with deterministic prompts resolves this, and a speaker-specific LoRA stage trained on 3.5 h of single-speaker data anchors identity while updating ~5% of parameters. Our system achieves WER 10.7% (2.9 above the ASR floor), MOS-I 4.00 (vs. 4.36 human speech), and near-human speaker consistency (MOS-C 4.24 vs. 4.30), showing that robust single-speaker Greek TTS is achievable with limited curated data.

Abstract

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