fix(bridge): gate STT on real speech so noise doesn't trigger replies

The bridge transcribe path joined every Whisper segment unconditionally, so a
brief loud sound or background noise that momentarily opened the mic gate (no
real speech) still produced a transcript, and Whisper's noise hallucinations
("감사합니다", "MBC 뉴스", ...) made the bot reply to nothing.

Add bridge/stt_filter.py mirroring the desktop listener's _filter_noisy_segments
policy: a hard no_speech_prob cutoff (whisper_no_speech_threshold) plus an
avg_logprob confidence floor (whisper_min_confidence), both config-driven. Apply
it in transcribe() so only segments that look like human speech survive; a
noise-only turn yields an empty transcript and the existing empty-transcript
guard drops it with no reply. Add unit tests for the gate.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

tests/test_bridge_stt_filter.py

@@ -0,0 +1,102 @@
+"""Unit tests for the bridge STT speech gate.
+
+The gate decides whether a Whisper segment is real human speech or just noise /
+a brief loud blip that Whisper hallucinated text from. Only speech should reach
+the reply engine, so a noisy mic that momentarily opens without anyone speaking
+produces no transcript and no reply. Thresholds are config-driven, so the tests
+pass explicit references rather than hardcoding the production defaults.
+"""
+
+import pytest
+
+from bridge.stt_filter import (
+    filter_speech_segments,
+    is_non_speech,
+    segment_confidence,
+)
+
+
+class Seg:
+    """Minimal stand-in for a faster-whisper segment."""
+
+    def __init__(self, text, no_speech_prob=0.0, avg_logprob=0.0):
+        self.text = text
+        self.no_speech_prob = no_speech_prob
+        self.avg_logprob = avg_logprob
+
+
+@pytest.mark.unit
+def test_real_speech_is_kept():
+    seg = Seg("오늘 일정 알려줘", no_speech_prob=0.02, avg_logprob=-0.2)
+    assert filter_speech_segments(
+        [seg], no_speech_threshold=0.5, min_confidence=0.3
+    ) == [seg]
+
+
+@pytest.mark.unit
+def test_noise_with_high_no_speech_prob_is_dropped():
+    # A mic blip Whisper hallucinated "감사합니다" from: not speech.
+    seg = Seg("감사합니다", no_speech_prob=0.92, avg_logprob=0.5)
+    assert filter_speech_segments(
+        [seg], no_speech_threshold=0.5, min_confidence=0.3
+    ) == []
+
+
+@pytest.mark.unit
+def test_no_speech_cutoff_runs_before_the_confidence_check():
+    # Confident hallucination: high avg_logprob but also high no_speech_prob.
+    # The no-speech cutoff must catch it regardless of confidence.
+    seg = Seg("MBC 뉴스", no_speech_prob=0.8, avg_logprob=0.9)
+    assert filter_speech_segments(
+        [seg], no_speech_threshold=0.5, min_confidence=0.3
+    ) == []
+
+
+@pytest.mark.unit
+def test_low_confidence_decode_is_dropped():
+    # avg_logprob -0.8 -> confidence 0.2, below the 0.3 floor.
+    seg = Seg("어버버", no_speech_prob=0.1, avg_logprob=-0.8)
+    assert filter_speech_segments(
+        [seg], no_speech_threshold=0.5, min_confidence=0.3
+    ) == []
+
+
+@pytest.mark.unit
+def test_order_preserved_dropping_only_non_speech():
+    a = Seg("진짜 말한 문장", no_speech_prob=0.05, avg_logprob=-0.1)
+    noise = Seg("음", no_speech_prob=0.95, avg_logprob=0.4)
+    b = Seg("두 번째 문장", no_speech_prob=0.05, avg_logprob=-0.1)
+    kept = filter_speech_segments(
+        [a, noise, b], no_speech_threshold=0.5, min_confidence=0.3
+    )
+    assert kept == [a, b]
+
+
+@pytest.mark.unit
+def test_segments_missing_metadata_are_kept():
+    # No no_speech_prob / avg_logprob -> we can't prove it's noise, so keep it.
+    class Bare:
+        text = "메타데이터 없는 문장"
+
+    seg = Bare()
+    assert filter_speech_segments(
+        [seg], no_speech_threshold=0.5, min_confidence=0.3
+    ) == [seg]
+
+
+@pytest.mark.unit
+def test_is_non_speech_uses_an_inclusive_threshold():
+    assert is_non_speech(0.5, 0.5) is True
+    assert is_non_speech(0.49, 0.5) is False
+
+
+@pytest.mark.unit
+def test_segment_confidence_prefers_avg_logprob():
+    assert segment_confidence(Seg("x", avg_logprob=-0.2)) == pytest.approx(0.8)
+    # Falls back to 1 - no_speech_prob when avg_logprob is absent.
+
+    class NoLogprob:
+        text = "x"
+        no_speech_prob = 0.25
+
+    assert segment_confidence(NoLogprob()) == pytest.approx(0.75)