diff --git a/src/pipecat/services/whisper/utils.py b/src/pipecat/services/whisper/utils.py
index 1d8c84897..b28c945c0 100644
--- a/src/pipecat/services/whisper/utils.py
+++ b/src/pipecat/services/whisper/utils.py
@@ -4,7 +4,7 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-"""Utility functions for extracting probability metrics from Whisper-based STT services."""
+"""Utility functions for extracting probability metrics from STT services."""
 
 import math
 from typing import Optional
@@ -27,17 +27,18 @@ def extract_whisper_probability(frame: TranscriptionFrame) -> Optional[float]:
     Returns:
         Probability (0-1) if available, None otherwise.
 
-    Example:
-        >>> from pipecat.services.groq.stt import GroqSTTService
-        >>> from pipecat.services.whisper.utils import extract_whisper_probability
-        >>>
-        >>> stt = GroqSTTService(include_prob_metrics=True)
-        >>> # ... use stt in pipeline ...
-        >>> # In your frame processor:
-        >>> if isinstance(frame, TranscriptionFrame):
-        >>>     prob = extract_whisper_probability(frame)
-        >>>     if prob:
-        >>>         print(f"Transcription confidence: {prob:.2%}")
+    Example::
+
+        from pipecat.services.groq.stt import GroqSTTService
+        from pipecat.services.whisper.utils import extract_whisper_probability
+
+        stt = GroqSTTService(include_prob_metrics=True)
+        # ... use stt in pipeline ...
+        # In your frame processor:
+        if isinstance(frame, TranscriptionFrame):
+            prob = extract_whisper_probability(frame)
+            if prob:
+                print(f"Transcription confidence: {prob:.2%}")
     """
     if not frame.result:
         return None
@@ -52,36 +53,83 @@ def extract_whisper_probability(frame: TranscriptionFrame) -> Optional[float]:
     return None
 
 
-def extract_openai_gpt4o_logprobs(frame: TranscriptionFrame) -> Optional[list]:
-    """Extract logprobs from OpenAI GPT-4o-transcribe TranscriptionFrame result.
+def extract_openai_gpt4o_probability(frame: TranscriptionFrame) -> Optional[float]:
+    """Extract probability from OpenAI GPT-4o-transcribe TranscriptionFrame result.
 
     Args:
         frame: TranscriptionFrame with result from OpenAISTTService
             using GPT-4o-transcribe model (when include_prob_metrics=True).
 
     Returns:
-        List of logprobs if available, None otherwise.
+        Probability (0-1) if available, None otherwise.
 
-    Example:
-        >>> from pipecat.services.openai.stt import OpenAISTTService
-        >>> from pipecat.services.whisper.utils import extract_openai_gpt4o_logprobs
-        >>>
-        >>> stt = OpenAISTTService(model="gpt-4o-transcribe", include_prob_metrics=True)
-        >>> # ... use stt in pipeline ...
-        >>> # In your frame processor:
-        >>> if isinstance(frame, TranscriptionFrame):
-        >>>     logprobs = extract_openai_gpt4o_logprobs(frame)
-        >>>     if logprobs:
-        >>>         # Calculate average logprob
-        >>>         avg_logprob = sum(logprobs) / len(logprobs)
-        >>>         prob = math.exp(avg_logprob)
-        >>>         print(f"Transcription confidence: {prob:.2%}")
+    Example::
+
+        from pipecat.services.openai.stt import OpenAISTTService
+        from pipecat.services.whisper.utils import extract_openai_gpt4o_probability
+
+        stt = OpenAISTTService(model="gpt-4o-transcribe", include_prob_metrics=True)
+        # ... use stt in pipeline ...
+        # In your frame processor:
+        if isinstance(frame, TranscriptionFrame):
+            prob = extract_openai_gpt4o_probability(frame)
+            if prob:
+                print(f"Transcription confidence: {prob:.2%}")
     """
     if not frame.result:
         return None
 
     # OpenAI GPT-4o-transcribe format: response.logprobs
     if hasattr(frame.result, "logprobs"):
-        return frame.result.logprobs
+        logprobs = frame.result.logprobs
+        if logprobs:
+            # Calculate average logprob and convert to probability
+            avg_logprob = sum(logprobs) / len(logprobs)
+            return math.exp(avg_logprob)
+
+    return None
+
+
+def extract_deepgram_probability(frame: TranscriptionFrame) -> Optional[float]:
+    """Extract probability from Deepgram TranscriptionFrame result.
+
+    Args:
+        frame: TranscriptionFrame with result from DeepgramSTTService.
+
+    Returns:
+        Probability (0-1) if available, None otherwise.
+        Returns alternative-level confidence if available, otherwise calculates
+        average confidence from word-level confidences.
+
+    Example::
+
+        from pipecat.services.deepgram.stt import DeepgramSTTService
+        from pipecat.services.whisper.utils import extract_deepgram_probability
+
+        stt = DeepgramSTTService()
+        # ... use stt in pipeline ...
+        # In your frame processor:
+        if isinstance(frame, TranscriptionFrame):
+            prob = extract_deepgram_probability(frame)
+            if prob:
+                print(f"Transcription confidence: {prob:.2%}")
+    """
+    if not frame.result:
+        return None
+
+    result = frame.result
+    if hasattr(result, "channel") and result.channel:
+        if hasattr(result.channel, "alternatives") and result.channel.alternatives:
+            alt = result.channel.alternatives[0]
+            conf = getattr(alt, "confidence", None)
+            if conf is not None:
+                return float(conf)
+
+            words = getattr(alt, "words", None)
+            if words:
+                word_confs = [getattr(w, "confidence", None) for w in words]
+                word_confs = [c for c in word_confs if c is not None]
+                if word_confs:
+                    return float(sum(word_confs) / len(word_confs))
 
     return None