diff --git a/inference-engine/ie_bridges/python/sample/speech_sample/speech_sample.py b/inference-engine/ie_bridges/python/sample/speech_sample/speech_sample.py
index bb2fa27e43b017..67601e1f3794be 100755
--- a/inference-engine/ie_bridges/python/sample/speech_sample/speech_sample.py
+++ b/inference-engine/ie_bridges/python/sample/speech_sample/speech_sample.py
@@ -228,10 +228,9 @@ def main():
 # ---------------------------Step 7. Do inference----------------------------------------------------------------------
     log.info('Starting inference in synchronous mode')
     results = {blob_name: {} for blob_name in output_blobs}
-    infer_times = []
-    perf_counters = []
+    total_infer_time = 0
 
-    for key in sorted(input_data):
+    for i, key in enumerate(sorted(input_data)):
         start_infer_time = default_timer()
 
         # Reset states between utterance inferences to remove a memory impact
@@ -244,43 +243,49 @@ def main():
         for blob_name in result.keys():
             results[blob_name][key] = result[blob_name]
 
-        infer_times.append(default_timer() - start_infer_time)
-        perf_counters.append(exec_net.requests[0].get_perf_counts())
+        infer_time = default_timer() - start_infer_time
+        total_infer_time += infer_time
+        num_of_frames = file_data[0][key].shape[0]
+        avg_infer_time_per_frame = infer_time / num_of_frames
 
 # ---------------------------Step 8. Process output--------------------------------------------------------------------
-    for blob_name in output_blobs:
-        for i, key in enumerate(sorted(results[blob_name])):
-            log.info(f'Utterance {i} ({key})')
+        log.info('')
+        log.info(f'Utterance {i} ({key}):')
+        log.info(f'Total time in Infer (HW and SW): {infer_time * 1000:.2f}ms')
+        log.info(f'Frames in utterance: {num_of_frames}')
+        log.info(f'Average Infer time per frame: {avg_infer_time_per_frame * 1000:.2f}ms')
+
+        for blob_name in output_blobs:
+            log.info('')
             log.info(f'Output blob name: {blob_name}')
-            log.info(f'Frames in utterance: {results[blob_name][key].shape[0]}')
-            log.info(f'Total time in Infer (HW and SW): {infer_times[i] * 1000:.2f}ms')
+            log.info(f'Number scores per frame: {results[blob_name][key].shape[1]}')
 
             if args.reference:
+                log.info('')
                 compare_with_reference(results[blob_name][key], references[blob_name][key])
 
-            if args.performance_counter:
-                if 'GNA' in args.device:
-                    pc = perf_counters[i]
-                    total_cycles = int(pc['1.1 Total scoring time in HW']['real_time'])
-                    stall_cycles = int(pc['1.2 Stall scoring time in HW']['real_time'])
-                    active_cycles = total_cycles - stall_cycles
-                    frequency = 10**6
-                    if args.arch == 'CORE':
-                        frequency *= GNA_CORE_FREQUENCY
-                    else:
-                        frequency *= GNA_ATOM_FREQUENCY
-                    total_inference_time = total_cycles / frequency
-                    active_time = active_cycles / frequency
-                    stall_time = stall_cycles / frequency
-                    log.info('')
-                    log.info('Performance Statistics of GNA Hardware')
-                    log.info(f'   Total Inference Time: {(total_inference_time * 1000):.4f} ms')
-                    log.info(f'   Active Time: {(active_time * 1000):.4f} ms')
-                    log.info(f'   Stall Time:  {(stall_time * 1000):.4f} ms')
-
-            log.info('')
-
-    log.info(f'Total sample time: {sum(infer_times) * 1000:.2f}ms')
+        if args.performance_counter:
+            if 'GNA' in args.device:
+                pc = exec_net.requests[0].get_perf_counts()
+                total_cycles = int(pc['1.1 Total scoring time in HW']['real_time'])
+                stall_cycles = int(pc['1.2 Stall scoring time in HW']['real_time'])
+                active_cycles = total_cycles - stall_cycles
+                frequency = 10**6
+                if args.arch == 'CORE':
+                    frequency *= GNA_CORE_FREQUENCY
+                else:
+                    frequency *= GNA_ATOM_FREQUENCY
+                total_inference_time = total_cycles / frequency
+                active_time = active_cycles / frequency
+                stall_time = stall_cycles / frequency
+                log.info('')
+                log.info('Performance Statistics of GNA Hardware')
+                log.info(f'   Total Inference Time: {(total_inference_time * 1000):.4f} ms')
+                log.info(f'   Active Time: {(active_time * 1000):.4f} ms')
+                log.info(f'   Stall Time:  {(stall_time * 1000):.4f} ms')
+
+    log.info('')
+    log.info(f'Total sample time: {total_infer_time * 1000:.2f}ms')
 
     if args.output:
         for i, blob_name in enumerate(results):